
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39227664
71302
10.1038/s41598-024-71302-9
Article
Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model
Alotaibi Moneerah 1
Alshardan Amal 2
Maashi Mashael 3
Asiri Mashael M. abusharara@kku.edu.sa

4
Alotaibi Sultan Refa 1
Yafoz Ayman 5
Alsini Raed 5
Khadidos Alaa O. 5
1 https://ror.org/05hawb687 grid.449644.f 0000 0004 0441 5692 Department of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Shaqra, Saudi Arabia
2 https://ror.org/05b0cyh02 grid.449346.8 0000 0004 0501 7602 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671 Riyadh, Saudi Arabia
3 https://ror.org/02f81g417 grid.56302.32 0000 0004 1773 5396 Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, 11543 Riyadh, Saudi Arabia
4 https://ror.org/052kwzs30 grid.412144.6 0000 0004 1790 7100 Department of Computer Science, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia
5 https://ror.org/02ma4wv74 grid.412125.1 0000 0001 0619 1117 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
3 9 2024
3 9 2024
2024
14 2043430 6 2024
27 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Cancer seems to have a vast number of deaths due to its heterogeneity, aggressiveness, and significant propensity for metastasis. The predominant categories of cancer that may affect males and females and occur worldwide are colon and lung cancer. A precise and on-time analysis of this cancer can increase the survival rate and improve the appropriate treatment characteristics. An efficient and effective method for the speedy and accurate recognition of tumours in the colon and lung areas is provided as an alternative to cancer recognition methods. Earlier diagnosis of the disease on the front drastically reduces the chance of death. Machine learning (ML) and deep learning (DL) approaches can accelerate this cancer diagnosis, facilitating researcher workers to study a vast majority of patients in a limited period and at a low cost. This research presents Histopathological Imaging for the Early Detection of Lung and Colon Cancer via Ensemble DL (HIELCC-EDL) model. The HIELCC-EDL technique utilizes histopathological images to identify lung and colon cancer (LCC). To achieve this, the HIELCC-EDL technique uses the Wiener filtering (WF) method for noise elimination. In addition, the HIELCC-EDL model uses the channel attention Residual Network (CA-ResNet50) model for learning complex feature patterns. Moreover, the hyperparameter selection of the CA-ResNet50 model is performed using the tuna swarm optimization (TSO) technique. Finally, the detection of LCC is achieved by using the ensemble of three classifiers such as extreme learning machine (ELM), competitive neural networks (CNNs), and long short-term memory (LSTM). To illustrate the promising performance of the HIELCC-EDL model, a complete set of experimentations was performed on a benchmark dataset. The experimental validation of the HIELCC-EDL model portrayed a superior accuracy value of 99.60% over recent approaches.

Keywords

Lung and colon cancer
Histopathological imaging
Deep learning
Tuna swarm optimization
Machine learning
Subject terms

Computer science
Information technology
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Cancer is one of the primary origins of death worldwide, as per the WHO1. LCC are the main dominant kinds, after breast cancer (BC), with rates of 10% and 11.4%, correspondingly, in the year 2020. At the same time, there is a possibility of synchronous occurrence among LCCs2. Furthermore, LCC shows the best dual mortality rates of 9.4% and 18%, respectively, compared to all cancers. So, a more precise analysis of these cancer sub-types was essential for discovering the treatment choices at the prompt step of the illness3. Cancer analysis includes numerous key stages to identify the cancer cells and define the phase and kind of tumour. Once the cancer is assumed, a doctor generally executes a physical analysis. Imaging analysis, such as MRI, computed tomography (CT), ultrasound, mammography, or radiography, is executed to imagine interior organs and tissues4. When the doubtful zones were recognized, a biopsy might be executed to eliminate a tissue sample for analysis5. This analysis of tissue is recognized as a histopathological study and includes inspecting a tissue sample below a microscope. Skilled doctors will mainly achieve the histopathological analysis by examining images from numerous biopsies6. The main objective is to identify shapes, forms, and sizes of related structures in the tissue or any other aspects that are exact to definite illnesses7. To discover cancer in a tissue, the professional should distinguish between healthy and tumorous cells. The classification of histopathology images for LCC has numerous lasting challenges that must be solved8.

Firstly, the main obstacle is the latent misclassification owing to the overlying histopathological features of dissimilar LC sub-types (like lung squamous cell carcinoma and lung adenocarcinoma), which is mainly challenging for skilled doctors to distinguish among them9. Therefore, a convolutional neural network (CNN) can precisely differentiate amid such sub-types, which is crucial for reliable analysis. Next, the need for available datasets projects a difficult task in the emerging CNN method for histopathological imagery analysis, as the technique cannot precisely acquire the distinguishing features of numerous LCC imageries10. DL provides a possible therapy for this difficulty. Pathology can use ML for multiple uses, like disease analysis. Automating the identification of histopathological imageries utilizing ML techniques might precisely identify illnesses and meet the needs of massive datasets with the highest accuracy and other standards11. Moreover, transfer learning (TL) executes a dissimilar technique to solve cross-disciplinary data attainment tasks by leveraging data from the present dataset. The scratch CNN application gives assured merits compared to TL methods12. Addressing the critical requirement for enhanced early recognition techniques is crucial to global cancer mortality. Despite their prevalence and high mortality rates, current diagnostic challenges continue, comprising synchronous occurrences. Early recognition enhances treatment outcomes and survival rates13. By employing ensemble DL models and histopathological imaging data, this study aims to present more precise diagnostic tools capable of recognizing LCC in its initial stages, easing timely interventions and ultimately enhancing patient outcomes14.

This research presents Histopathological Imaging for the Early Detection of Lung and Colon Cancer via Ensemble DL (HIELCC-EDL) model. The HIELCC-EDL technique utilizes histopathological images to identify lung and colon cancer (LCC). To achieve this, the HIELCC-EDL technique uses the Wiener filtering (WF) method for noise elimination. In addition, the HIELCC-EDL model uses the channel attention Residual Network (CA-ResNet50) model for learning complex feature patterns. Moreover, the hyperparameter selection of the CA-ResNet50 model is performed using the tuna swarm optimization (TSO) technique. Finally, the detection of LCC is achieved by using the ensemble of three classifiers such as extreme learning machine (ELM), competitive neural networks (CNNs), and long short-term memory (LSTM). To illustrate the promising performance of the HIELCC-EDL model, a complete set of experimentations was performed on a benchmark dataset. The crucial contribution of the HIELCC-EDL model is listed below:The HIELCC-EDL technique is specifically constructed to detect the LCC accurately via histopathological images. It also introduced the incorporation of state-of-the-art models not priorly integrated in this domain. This method also integrates advanced feature extraction approaches with robust classification models, promising greater effectualness and accuracy in cancer recognition than conventional methods.

WF, a primary signal processing technique, can preprocess histopathological images by effectively mitigating noise. This crucial step substantially improves the quality of input data, thereby enhancing the reliability and accuracy of subsequent methods constructed to detect LCC. This technique underlines the significance of noise removal in optimizing recognition results from medical imaging data.

Implementing the CA-ResNet50 mode, a refined version of ResNet50 incorporates channel attention mechanisms to extract complex feature patterns from histopathological images robustly. This method accentuates the improvement of feature representation by selectively highlighting relevant data while suppressing noise and irrelevant insights, aiming to enhance the accuracy of LCC detection methods. This methodological option underlines a strategic focus on optimizing diagnostic precision via advanced feature-learning strategies.

An ensemble strategy is employed by integrating three classifiers: the ELM for effectual learning from high-dimensional data, CNNs to improve pattern classification diversity, and LSTM networks to handle sequential data and temporal patterns. This ensemble model aims to utilize the merits of every classifier to attain robust and precise recognition of LCC from histopathological images, accentuating an overall model for incorporating diverse learning paradigms in medical image evaluation.

Integrating TSO for hyperparameter tuning of the CA-ResNet50 method presents a novel model motivated by tuna fish behaviour, potentially improving convergence speed and thoroughly exploring hyperparameter settings. This novel technique contrasts with conventional grid search or random search methods, suggesting a more efficient and effective means to maximize model accomplishment for precise recognition of LCC from histopathological images. This incorporation underscores a forward-looking strategy in employing nature-inspired models for medical image evaluation

Related works

Current trends in early cancer detection: a review

In recent years, using histopathological images for early LCC recognition has witnessed substantial enhancements driven by DL models. Several studies have explored the utilization of CNNs and ensemble learning methods to enhance diagnostic accuracy. For instance, studies have used TL from pre-trained models like ResNet and VGG to extract meaningful features from histopathological images, improving the capability to discriminate cancerous tissue from healthy tissue. Furthermore, ensemble techniques integrating several classifiers, namely SVMs, decision trees, and neural networks, are also employed to refine classification results. However, while these methodologies show promise, difficulties still must be solved in optimizing model performance, robustness across several datasets, and clinical applicability. This section reviews recent developments and models utilized in the field, laying the groundwork for the presented ensemble DL model aimed at improving early cancer recognition abilities using histopathological imaging.

Recent advances in histopathological imaging for cancer detection

In15, a computational structure was projected to describe hybrid methods over deep features by TL, assortment by grading, and a robust ensemble classifier with five techniques. The techniques that were attained were functional in categorizing histological imageries. Then, the features were definite, utilizing layers from ResNet50 and AlexNet structures. The attributes were planned into sub-sets and yielded to a k-fold cross-validation procedure. Abdullah and Ragab16 projected a meta-heuristics model with deep CNN-based colon cancer identification based on histopathological image data (MDCNN-C3HI). At first, the model uses a bilateral filtering (BF) method for eliminating the sound. Next, the developed model utilizes improved capsule networks with the Adam optimizer (AO) for the removal of the feature vector. For the identification of CRC, the model utilizes DL-altered classifiers of neural networks, while the tunicate swarm technique was employed to perfect its hyperparameter. In17, a structure based on many lightweight DL methods has been developed. The framework uses numerous transformation techniques. The deep features attained from these techniques were then reduced by utilizing PCA and fast Walsh–Hadamard transform (FHWT) models. Afterwards, DWT combined the FWHT-decreased features achieved from the 3 DL methods. Lastly, the reduced features as an outcome of FHWT-DWT and PCA reduction and fusion procedures are served to 4 dissimilar ML techniques.

In18, a proposed technique depends upon ensemble learning (EL) to categorize histology tissues marked with eosin and hematoxylin. The EL considered many combinations of methods generally employed to progress computer-aided diagnosis (CAD) models in medicinal images. The feature extraction has been definite with dissimilar descriptors, and the handcrafted and DL techniques have been discovered. The features of DL were attained by utilizing five dissimilar CNN structures. Muneer et al.19 developed a dual method, a radiomic-based SVM technique, and a DL method for recognizing dissimilar cells in colon cancer utilizing pathological imageries. The radiomics features were initially removed from the histopathology imageries, and SVM was employed to identify CRC. The 2nd method removed the DL feature and categorized the CRC utilizing ResNet18. In20, an assortment of models was used. DL models were employed in this research paper. Also, a DL technique is projected. CNN model employed for analyzing compound data. The research examined the compound tumour imageries using the CNN method to classify suspicious or abnormal cancer patterns. In addition, the technique created a 5-layer DNN method. It contains input, output, and four hidden layers (HLs). The method employed ReLU in HL and the Softmax in the output layer.

In21, an ensemble DL model has been projected. At first, three deep CNN TL models, MobileNet-V2, VGG-19, and Resnet-50, were employed separately for identification. This technique influences the Prowers of MobileNet-V2, VGG-19, and ResNet-50’s pre-trained weights for feature extractor, and then removed features are arranged and utilized for classification over the weighted average ensemble model. Alqahtani et al.22 presented an Improved Water Strider Algorithm with Convolutional Autoencoder for LCC Detection (IWSACAE-LCCD) on HI. The median filtering (MF) method can be employed for the noise deletion. Also, the MobileNet-v2 technique is utilized as a feature extraction. Lastly, the CAE method is used to identify the presence of LCC. Table 1 summarizes the existing studies on LCC detection.Table 1 Summary of existing studies on LCC detection.

Reference number	Objective	Method	Dataset	Measures	
De Oliveira et al.15	To develop and evaluate hybrid models for computer-aided diagnosis	AlexNet, ResNet-50, k-fold Cross-Validation	UCSB and LG	Standard measures	
Abdullah and Ragab16	To present a model for effectual colorectal cancer classification from histopathological imaging data	Bilateral filtering, capsule network with the Adam optimizer, tunicate swarm algorithm	Benchmark dataset	Accuracy, sensitivity, specificity	
Attallah, Aslan and Sabanci17	To develop a model for early detection of LCCs	ShuffleNet, MobileNet, SqueezeNet, principal component analysis, and fast Walsh–Hadamard transform, discrete wavelet transform	LC25000	Precision, accuracy, F1-score, sensitivity, MCC, and specificity	
Tenguam et al.18	To present a model to classify histology tissues stained with hematoxylin and eosin	Random forest, support vector machine, K-nearest neighbors, logistic regression, and Naive Bayes	H&E	Benchmark measures	
Muneer et al.19	To propose a method to detect diverse kinds of cells in colorectal cancer utilizing pathological images	Radiomic-based Support vector machine, deep learning, Res-Net-18	Dataset of 5000 Pathological Images of Colorectal Cancer	Recall, precision, F1-score, and accuracy	
Mohalder et al.20	To predict CRC tumours from histopathological images	Convolutional Neural network, deep neural network	Same dimension of colon cancer tissues histopathological images	Precision, Recall, and F1-score	
Sultana et al.21	To detect and classify lung cancers	MobileNetV2, VGG19, and Resnet50	Standard dataset	Benchmark measures	
Alqahtani et al.22	To detect LCC	Median filtering, Mobilenetv2, improved water strider algorithm, convolutional autoencoder	Accuracy, precision, recall, F-score, and AUC-score	Diverse dataset of higher-quality HIs	

Methodology

This study presents a new HIELCC-EDL technique. This technique uses histopathological images to identify LCC. To accomplish this, the method involves distinct stages, such as image preprocessing, feature extractor, TSO-based parameter selection, and EL. Figure 1 represents the workflow of the HIELCC-EDL model.Fig. 1 Workflow of HIELCC-EDL model.

Implementation data

The HIELCC-EDL approach employed a benchmark dataset23 and is demonstrated in Table 2. All images are 768 × 768 pixels and in JPEG file format. The images were produced from an original sample of HIPAA-compliant. They validated sources comprising 750 images of lung tissue (250 benign lung tissue, 250 lung adenocarcinomas, and 250 lung squamous cell carcinomas) and 500 images of colon tissue (250 benign colon tissue and 250 colon adenocarcinomas), which were augmented to 25,000 by employing the Augmentor package.Table 2 Details of the dataset.

Classes	Descriptions	Overall instances	
Con-Ade	Colon adenocarcinoma	5000	
Con-BeT	Colon benign tissue	5000	
Lug-Ade	Lung adenocarcinoma	5000	
Lug-BeT	Lung benign tissue	5000	
Lug-Sec	Lung squamous cell carcinoma	5000	
Total number of instances	25,000	

There are five classes in the dataset, each with 5000 images, such as:Lung benign tissue

Lung adenocarcinoma

Lung squamous cell carcinoma

Colon adenocarcinoma

Colon benign tissue

Image preprocessing

At the primary stage, the HIELCC-EDL model uses the WF technique for the noise elimination process. WF is an innovative image preprocessing model intended for noise removal and signal renovation, depending upon statistical values24. Its goal is to diminish the mean square error among the original and filtered images and uncorrupted images by reflecting both the degradation function and the statistical features of noise and the image itself. Unlike modest smoothing filters, WF adjusts its behaviour based on local image alteration, permitting it to efficiently decrease noise while maintaining significant image details like textures and edges. This adaptability makes WF mainly effective in handling Gaussian and other general noise, improving image clarity in applications ranging from digital photography to medicinal imaging and remote sensing, where exact detail retention is vital. Table 3 portrays a table for the train-validation-test split.Table 3 Dataset split for histopathological images.

Dataset split	Colon adenocarcinoma	Colon benign tissue	Lung adenocarcinoma	Lung benign tissue	Lung squamous cell carcinoma	
Training (TR)	1600	1600	1600	1600	1600	
Validation (VL)	400	400	400	400	400	
Test	600	600	600	600	600	
Total	2500	2500	2500	2500	2500	

Feature extractor

Next, the HIELCC-EDL technique uses the CA-ResNet50 model for learning complex feature patterns. The fundamental notion of ResNet is to bring a unique shortcut link25. It uses a skip connection model connecting multiple layers with batch normalization and ReLU. Instead of allowing the system to study the underlying mapping, it enables it to match the residual mapping. For instance, assume Hx as a fundamental mapping, which fits by a stacked layer, with “x” representing the input as an initial layer. The non-linear layer can gradually approximate a complex function, similar to the approximate residual function as Fx:=Hx-x. Thus, the original function becomes Fx+x. The learning curves for each method might fluctuate even though it can asymptotically approximate the desired function. Figure 2 depicts the structure of the CA-ResNet50 technique.Fig. 2 Architecture of CA-ResNet50 method.

The skip connection has the benefit of using all layers that degrade the model performance, which would be skipped by regularization. Therefore, the training of DNN is not hampered by gradient vanishing as in the classical CNN model. The parametric gate is used similarly to the LSTM network in these skip connections. This gate controls the amount of data that passes through the skip connection. ResNet resolves the feature map and gradient vanishing problems while training a significant quantity of DCNN. Backpropagation is more fluid because the gradients can access a further shortcut network. ResNet50 is a typical deep ResNet that strikes the right balance between training efficiency and network depth. The conventional ResNet50 model was initially trained using images from the ImageNet database.

The attention module has been examined and used in the DL field to enhance performance given an input image of feature mapping as a feature detector. The spatial length of input features is squeezed to calculate the channel attention effectively. The average and max pooling are utilized to organize and gather better channel‐wise attention. Then, these feature descriptors, Favgc and Fmaxc, are sent to a network for producing the channel attention mapping: Mc∈RC×1×1. The network has an MLP with a single HL. The output feature vector is combined using component‐wise summation.1 McF=σMLPAvgPoolF+MLPMaxPoolF=σW1W0Favgc+W1W0Fmaxc

where W0∈RCr×C and W1∈RC×Cr represent the MLP weights, σ denotes the activation function of the sigmoid, W0 and W1 refer to the shared weight, and W0 denotes the activation function of ReLu.

Parameter selection

At this stage, the hyperparameter selection of the CA-ResNet50 model can be carried out using the TSO method. Tunas is also called Thunnini, one of the aquatic meat‐eating fish26–28. The dimension varies extensively among every kind of fish. Tuna is the main sea hunter, feeding a range of midwater and shallow fishes. They are sturdy swimmers who have an individual and effectual swimming model, the so-called fish-tail method. The tuna uses the “group travel” method for hunting. They mainly use their skill to find and attack their searches. These animals generally have a variety of effects and smart food-seeking tactics. The primary method is spiral food seeking, and 2nd method is parabolic food seeking. Each tuna swims later than the previous by creating an illustrative method to set its prey. The novel swarm‐based metaheuristic optimizer technique called TSO has been presented based on signifying these standard food-seeking methods. The projected algorithm’s scientific method is definite with its foremost feature.

Initialize

Similar to swarm‐based metaheuristics, TSO commences the optimizer procedure by creating the first population at stochastic:2 Ziint=r·u-l+lb,i=1,2,…,NP,1

whereas Ziint denotes the first individual, u and l represent the higher and lower bounds, respectively; N refers to the tuna population number, and an evenly spread stochastic vector is specified as r among 0 and 1.

Spiral food‐seeking

When herring, sardines, and other small fishes see predators, the entire school of fish processes a dense formation frequently to change the swimming direction. Then, it is difficult for predators to pay attention to a goal. In this instant, the tuna group follows the victim thus by creating a narrow spiral structure. The vast bulk of fish in the cluster has the least intellect of discovering the direction; when a small fish swims positively in a stated direction, the nearby fish change their direction one after the other, yield a large set, and search the prey.

Once the hunt is over, tuna clusters swap information with all. Every tuna will obey the former fish, simplifying the data spreading among adjacent tuna. The spiral food-seeking methods calculation is expressed below:3 ZiI+1=a1ZbestI+βZbestI-ZiI+a2ZiI,i=1a1ZbestI+βZbestI-ZiI+I1)+a2Zi-1I,i=2,3,…,N

4 a1=a+1-a×IImax

5 a2=1-a-1-a×IImax

6 β=ebl×cos2πb

7 l=e3cosImax+1/I-1π)

where ZiI+1 denotes the I+1 iteration of the ith individual, ZbestI refers to the present optimum individual, a1 and a2 signify the constants of weight, which switch the members’ preference to drive near the former member and optimum individual, a denotes the constant value used to describe the quantity, I signifies the present iteration count; Imax represents the max iteration; b refers to a stochastic amount, which is evenly spread among 0to1.

When the tuna hunt near the food, they have precious exploitation ability for the searching space near the food. Thus, making a stochastic coordinate a position point for the spiral hunt is reflected. This shortens each member’s ability to hunt for a wider space and delivers TSO global search ability. The numerical method was labelled below:8 ZiI+1=a1ZrandI+βZrandI-ZiI+a2ZiI,i=1a1ZrandI+βZrandI-ZiI+I1)+a2Zi-1I,i=2,3,…,N

where ZrandI refers to a reference point. Mainly, meta-heuristic techniques commonly do a wide global search in the preliminary stage and slowly modify to precise local exploitation. So, TSO modifies the spiral food-seeking reference point to the optimum member as the iteration rises. At last, the mathematical formulation of the final spiral food-seeking method is given below:9 a1ZbestI+βZbestI-ZiI+a2ZiI,i=1a1ZbestI+βZbestI-ZiI+a2Zi-1I,i=2,3,…,Nifrand<IImaxa1ZrandI+βZrandI-ZiI+a2ZiI,i=1ifrand≥IImaxa1ZrandI+βZrandI-ZiI+a2Zi-1I,i=2,3,…,N

Parabolic food seeking

Also, to create a spiral structure, tunas process illustrative supportive feeding. They yield a parabolic structure by reference points. Similarly, they benefit from hunting around themselves to search for food.

These two strategies were completed simultaneously, with the theory of 50% possibility for both. The mathematical formulation is given below:10 Ziint=ZbestI+rand·ZbestI-ZiI+F·p2·ZbestI-ZiIifrand<0.5F·p2·ZiI·ifrand<0.5

11 p=1-IImaxI/Imax

On the other hand, F denotes a stochastic amount, which can be both 1 and ‐l. Tuna search kindly through 2 food seeking techniques and find their target. Initially, the population was formed in the search space during the TSO process.

In each distinct round, each one chooses 1 or 2 food-seeking tactics for achieving or picking the replicate state in the search space. Parameter z is considered the parameter set reproduction test. In the optimizer process, TSO’s complete members are always rehabilitated and calculated until the last state is met, as well as the steady, objective function and optimum member return.

Fitness selection is a significant factor in the performance of the TSO technique. The hyperparameter procedure selection contains the solution encode technique to assess the efficiency of the candidate solution. In this paper, the TSO model considers accuracy the main standard for project FF. Its formulation is given below.12 Fitness=maxP

13 P=TPTP+FP

where TP and FP signifies the true and false positive values.

EL model

Finally, the detection of LCC takes place using the ensemble of three classifiers such as ELM, CNN, and LSTM.

ELM classifier

Huang et al. 2004 proposed ELM for training a SLFN model29. It quickly learns with some examples in a simple situation and has the benefits of low complexity and fast training speed. It resolves the problems of gradient models, namely inappropriate selection of learning rate, relatively complex structure, local minimization, and overfitting. Assume N as a total sample, and the ithn‐dimensional sample is represented by xm=xm1,xm2,⋯,xmn,m=1,2,⋯,N. T is the target matrix.14 fELMx=Hβ

15 H=ga1∗x1+b1…gaL∗x1+bL⋮⋮⋮ga1∗xN+b1…gaL∗xN+bL

16 β=H+T

17 H+=HT(HHT)-1

where H refers to the HL matrix; H+ depicts the generalized inverse matrix of H; β indicates the weight vector between the ith HL and the output; a shows the weight from the input layer to the HL; b denotes the threshold of HL; L denotes the number of HL nodes.

In classical ELM, since weight a from the input layer to the HL and the threshold b of the HL are randomly selected, with great randomness, ELM has poor responsibility to samples that do not appear in the training set, that is, insufficient generalization ability. Nowadays, for ML parameters that are difficult to choose, scholars often use heuristic evolutionary models with strong optimization ability to optimize parameters. Combining these two models can help better complete tasks such as ML prediction and classification, thus making them more accurate.

CNN classifier

CNN is a very simple neural network that contains dual layers and utilizes an unsupervised learning model for training purposes30. The inputs are said to be features, and the outputs are known as classes. The input layer is wholly linked to the output layer. A weight co-efficient illustrates every connection between the layers of output and input. In the output layer, the neurons contest amongst themselves if the input feature is employed to the network input. The difference is competitive learning, where the output neurons enclose to contest amongst themselves to acquire activated, and only a single neuron is activated whenever, as equated to Hebbian learning, where many single neurons are activated or dismissed wherever.

These networks utilize a winner‐takes‐all tactic, whereas only the weight related to the winner neuron is upgraded in an exact period, but further weights are not upgraded. This learning procedure gradually increases the correlation between the input and the equivalent winner neurons throughout the learning.

If the pattern is delivered to the layer of input, then the neuron in the output layer contests between itself to activate. The rubrics utilized to upgrade the weight are mentioned as follows. For winner neuron k output, the evaluation is done by18 Δwkj=ηxj-wkj,

whereas η denotes the learning rate, xj represents the jth input pattern, wkj signifies the weight connection among jth and kth neurons, and Δwkj refers to the calculated weight change.

If the kth output neuron drops at epoch p, then19 Δwkj=0.

Weight upgrade for kth neuron at epoch p+1 was attained utilizing the below-given calculation:20 wkjp+1=wkjp+Δwkj.

LSTM classifier

Input Gate defines which data is appropriate to the present time step and must be kept in the memory cell31. At the present input, it grabs input and the preceding HL and employs a sigmoid activation function for every component. For dual classification issues, sigmoid is generally utilized as an activation function. The mathematical calculation is signified as follows:21 i1=σWi1·Ht-1,xt+biasi1

where Wi1 denotes the weight matrix, i1, Ht-1 represents the preceding HL, xt refers to the present input, and biasi1 denotes a vector to enhance the method’s precision.

The 2nd layer signifies the computation of the candidate value, controlling the system by ensuring the preceding HL and present input into the tanh, as given below:22 i2=tanhWi2·Ht-1,xt+biasi2

These dual layers outputs are increased, and the data that wants to be restored in the memory cell outcomes:23 iinput=i1·i2

Forget Gate defines which data must be neglected or removed, depending upon the present input and the preceding HL. Its major part is to prevent the system from detecting unrelated or invalid data that might result from poor or overfitting efficiency. To attain this, the forget gate computes a vector value among 0 and 1, determining how many elements in the preceding long‐term memory (LTM) must be forgotten or preserved. The forgetting vector is generated by delivering the sequence of present input and preceding short‐term memory (STM) over a sigmoid. The values nearer to 0 representing the consistent component in the preceding LTM must be ignored, and those nearer to 1 indicate that the element must be conserved.

The forget vector ranges from 0 to 1, and the given formulation accurately denotes it:24 f=σWforgetHt-1,xt+biasforget

After the calculation of the forget vector, it is increased elementwise by the preceding LTM to get the novel LTM, which is given below:25 Ct=f⊙Ct-1

where Ct denotes the novel LTM, f denotes the vector of forgetting, ⊙ signifies the elementwise multiplication, and Ct-1 represents the preceding LTM.

Then, the novel LTM is upgraded with the data from the present input utilizing the gate of input that defines which elements of the present input must be inserted into the LTM.26 Ct=f⊙Ct-1+iinput

This procedure efficiently removes data from the preceding LTM because the present input is of no prolonged significance. By achieving this, the system can concentrate on the most significant feature of the input data and generate superior decisions or predictions.

Output gate

The output gate is a significant element that defines the LTM. The present input is permitted onto the subsequent cell or utilized as the concluding output. It is answerable for controlling the flow of data and temporary on appropriate data to the next time step or as output. It takes input at the present input, the preceding HL, and the present LTM, which can be handled by their relevant gates (i.e., forget and input), as described earlier.

Initially, the present input and the preceding HL are delivered into the sigmoid with the proper weights, which defines the ratio of the present LTM that must be added in the novel STM.27 O1=σWO1Ht-1′xt+biasO1

Next, the tanh is employed in the novel LTM, which was computed by the gate of forgetting and upgraded by the input gate. This controls the value of novel LTM.28 O2=tanhWO2·Ct+biasO2

Then, the standardized novel LTM is increased elementwise with the yield of the sigmoid to make the novel STM:

Availa29 Ht·Ot=O1⊙O2

The STM/HL and cell state/LTM delivered by these gates are delivered to the subsequent time step for the procedure to be repeated or employed as the last output. Figure 3 depicts the infrastructure of LSTM.Fig. 3 Architecture of LSTM.

Performance analysis

Implementation data

The performance validation of the HIELCC-EDL approach is executed on a benchmark dataset23.

Figure 4 portrays the sample images. The simulation of the proposed model is performed by utilizing the Python 3.6.5 tool on a PC with an i5-8600k, 250GB SSD, GeForce 1050Ti 4GB, 16GB RAM, and 1TB HDD. The parameter settings are: learning rate: 0.01, activation: ReLU, epoch count: 50, dropout: 0.5, and batch size: 5.Fig. 4 Sample images.

Results and discussion

In computing the performance of a classification model, various key metrics are utilized for assessing its efficiency. These comprises accuracy, precision, recall, F1 score, and AUC score. Every metric gives details into diverse factors of the performance of the model, namely its capability to precisely detect positive instances or its overall discriminatory power. True Positives (TP) are precisely predicted positive cases, while False Positives (FP) are incorrect positive predictions where negatives are incorrectly labeled as positives. True Negatives (TN) are precisely predicted negatives, and False Negatives (FN) are missed positives where the model fails to detect actual positive cases.

Metrics FormulasAccuracyAccuracy=TP+TNTotalPopulation

PrecisionPrecision=TPTP+FP

RecallRecall=TPTP+FN

F1-ScoreF1Score=2×Precision×RecallPrecision+Recall

AUC Score

The AUC score evaluates the capability of the technique for discriminating between classes. It ranges from 0 to 1, with 1 portraying precise classification and 0.5 depicting no discrimination (random guessing). The formula encompasses plotting the ROC curve and computing the area under this curve. It is not shown by a single formula but is evaluated via incorporation of the ROC curve.

These metrics collectively gives a comprehensive view of the performance of the model across several factors of prediction accuracy and robustness.

Figure 5 shows the confusion matrices produced by the HIELCC-EDL technique under 80:20 and 70:30 of TRAS/TESS. The outcomes depicts that the HIELCC-EDL technique has efficiently detected and classified the overall 5 classes precisely.Fig. 5 Confusion matrices of HIELCC-EDL technique (a, b) 80:20 and (c, d) 70:30 of TRAS/TESS.

In Table 4 and Fig. 6, the LCC detection outputs of the HIELCC-EDL approach are depicted under 80:20 of TRAS/TESS. The outputs signified that the HIELCC-EDL approach correctly identified five classes. With 80% TRAS, the HIELCC-EDL technique attains an average accuy of 99.57%, precn of 98.93%, recal of 98.93%, Fscore of 98.92%, and AUCscore of 99.33%. In addition, with 20%TESS, the HIELCC-EDL model gets an average accuy of 99.60%, precn of 99.00%, recal of 99.00%, Fscore of 99.00%, and AUCscore of 99.38%.Table 4 LCC detection outcome of HIELCC-EDL technique under 80:20 of TRAS/TESS.

Class labels	Accuy	Precn	Recal	FScore	AUCscore	
TRAS (80%)	
 Con-Ade	99.49	97.83	99.65	98.73	99.55	
 Con-BeT	99.70	99.26	99.23	99.24	99.52	
 Lug-Ade	99.65	98.86	99.40	99.13	99.56	
 Lug-BeT	99.32	99.64	96.95	98.28	98.43	
 Lug-Sec	99.70	99.08	99.40	99.24	99.58	
 Average	99.57	98.93	98.93	98.92	99.33	
TESS (20%)	
 Con-Ade	99.58	98.73	99.21	98.97	99.44	
 Con-BeT	99.68	98.78	99.59	99.18	99.65	
 Lug-Ade	99.64	98.81	99.40	99.11	99.55	
 Lug-BeT	99.36	99.69	97.11	98.38	98.52	
 Lug-Sec	99.74	99.01	99.70	99.35	99.73	
 Average	99.60	99.00	99.00	99.00	99.38	

Fig. 6 Average outcome of HIELCC-EDL technique under various measures.

In Fig. 7, the TR/VL accuracy outputs of the HIELCC-EDL technique are demonstrated under 80:20 of TRAS/TESS. The accuracy values are calculated throughout 0–25 epochs. The figure depicted that the TR/VL accuracy values display a rising tendency, indicating the HIELCC-EDL approach’s capacity with enhanced performance over several iterations. Moreover, the TR/VL accuracy remains closer over the epochs, which designates low least overfitting and exhibited the enhanced performance of the HIELCC-EDL approach, ensuring consistent forecasts on unseen samples.Fig. 7 Accuy curve of HIELCC-EDL method under 80:20 of TRAS/TESS.

In Fig. 8, the TR/VL loss graph of the HIELCC-EDL model is displayed under 80:20 of TRAS/TESS. The loss values are analyzed throughout 0–25 epochs. It is represented that the TR/VL accuracy values illustrate a decreasing tendency, notifying the capability of the ability of the HIELCC-EDL model to balance a trade-off between data fitting and generalization. The continual reduction in loss values guarantees the enhanced performance of the HIELCC-EDL approach and tunes the prediction results over time.Fig. 8 Loss curve of HIELCC-EDL method under 80:20 of TRAS/TESS.

In Fig. 9, the precision-recall (PR) curve analysis of the HIELCC-EDL method under 80:20 of TRAS/TESS interprets its performance by plotting Precision against Recall for every class. The outcome displays that the HIELCC-EDL method always accomplishes better PR values across dissimilar class labels, representing its capacity to maintain a significant portion of true positive forecasts between every positive prediction (precision) and seizing a considerable proportion of actual positives (recall). The stable rise in PR outcomes among all classes portrays the efficiency of the HIELCC-EDL technique in the classification process.Fig. 9 PR curve of HIELCC-EDL method under 80:20 of TRAS/TESS.

In Fig. 10, the ROC curve of the HIELCC-EDL method is studied under 80:20 of TRAS/TESS. The results imply that the HIELCC-EDL method attains improved ROC outcomes over every class, demonstrating a significant ability to discern the classes. This reliable trend of improved ROC values over various classes signifies the proficient performance of the HIELCC-EDL approach in predicting classes, highlighting the intense nature of the classification procedure.Fig. 10 ROC curve of HIELCC-EDL method under 80:20 of TRAS/TESS.

In Table 5 and Fig. 11, the LCC detection results of the HIELCC-EDL approach are portrayed under 70:30 of TRAS/TESS. The outcomes signified that the HIELCC-EDL approach properly recognized five classes. With 70%TRAS, the HIELCC-EDL model gets an average accuy of 99.24%, precn of 98.10%, recal of 98.10%, Fscore of 98.10%, and AUCscore of 98.81%. Furthermore, with 30%TESS, the HIELCC-EDL approach attains an average accuy of 99.21%, precn of 98.02%, recal of 98.02%, Fscore of 98.02%, and AUCscore of 98.76%.Table 5 LCC detection outcome of HIELCC-EDL method under 70:30 of TRAS/TESS.

Class labels	Accuy	Precn	Recal	FScore	AUCscore	
TRAS (70%)	
 Con-Ade	99.46	98.80	98.52	98.66	99.11	
 Con-BeT	99.15	98.53	97.22	97.87	98.43	
 Lug-Ade	99.14	97.52	98.14	97.83	98.76	
 Lug-BeT	99.17	97.60	98.25	97.92	98.82	
 Lug-Sec	99.27	98.03	98.36	98.20	98.93	
 Average	99.24	98.10	98.10	98.10	98.81	
TESS (30%)	
 Con-Ade	99.44	98.85	98.31	98.58	99.02	
 Con-BeT	99.21	97.90	98.10	98.00	98.79	
  Lug-Ade	99.15	97.95	97.95	97.95	98.70	
 Lug-BeT	98.96	97.69	97.17	97.43	98.29	
 Lug-Sec	99.27	97.69	98.56	98.12	99.00	
Average	99.21	98.02	98.02	98.02	98.76	

Fig. 11 Average outcome of HIELCC-EDL technique under various measures.

In Fig. 12, the TR/VL accuracy results of the HIELCC-EDL technique are demonstrated under 70:30 of TRAS/TESS. The accuracy values are evaluated on 0–25 epochs. The figure shows that the TR/VL accuracy values display a rising tendency, indicating the HIELCC-EDL technique’s skill with improved performance over several iterations. Additionally, the TR/VL accuracy remain closer over the epochs, indicating low minimal overfitting and exhibiting enhanced performance of the HIELCC-EDL approach, guaranteeing reliable forecasts on unseen samples.Fig. 12 Accuy curve of HIELCC-EDL technique under 70:30 of TRAS/TESS.

In Fig. 13, the TR/VL loss graph of the HIELCC-EDL model is displayed under 70:30 of TRAS/TESS. The loss values are evaluated on 0–25 epochs. It is represented that the TR/VL accuracy values illustrate a decreasing tendency, notifying the capability of the HIELCC-EDL model to balance a trade-off between data fitting and generalization. The frequent lessening in loss values additionally guarantees the enhanced performance of the HIELCC-EDL technique and tunes the prediction results over time.Fig. 13 Loss curve of HIELCC-EDL technique under 70:30 of TRAS/TESS.

In Fig. 14, the PR curve analysis of the HIELCC-EDL approach under 70:30 of TRAS/TESS offers an interpretation of its performance by plotting Precision against Recall for all the classes. The figure shows that the HIELCC-EDL model continuously accomplishes improved PR values across different class labels, indicating its ability to maintain a significant portion of true positive predictions amongst each positive prediction (precision) while also capturing a large proportion of actual positives (recall). The steady rise in PR outcomes among all classes portrays the effectiveness of the HIELCC-EDL model in the classification process.Fig. 14 PR curve of HIELCC-EDL technique under 70:30 of TRAS/TESS.

In Fig. 15, the ROC curve of the HIELCC-EDL technique is studied under 70:30 of TRAS/TESS. The results imply that the HIELCC-EDL model reaches enhanced ROC outcomes over each class, demonstrating significant capability of discriminating the classes. This reliable trend of improved ROC values over various classes signifies the proficient performance of the HIELCC-EDL model in predicting classes, highlighting the robust nature of the classification method.Fig. 15 ROC curve of HIELCC-EDL technique under 70:30 of TRAS/TESS.

In Table 6 and Fig. 16, a widespread comparison study of the HIELCC-EDL approach is clearly illustrated22. The results designate that the mSRC approach has shown ineffective performance. Along with that, the RESNET50, DAELGNN, and Faster R-CNN approaches have exhibited slightly boosted results. Meanwhile, the MPADL-LC3, BERTL-HTALCC, and TWSACAE-LCCD models have demonstrated moderately closer results. However, the HIELCC-EDL approach outperforms other methods with an increased accuy of 99.60%, precn of 99.00%, recal of 99.00%, and Fscore of 99.00%.Table 6 Comparative analysis of the HIELCC-EDL method with existing models22.

Models	Accuy	Precn	Recal	FScore	
HIELCC-EDL	99.60	99.00	99.00	99.00	
TWSACAE-LCCD	99.41	98.52	98.51	98.51	
BERTL-HTALCC	99.10	97.96	97.94	97.93	
MPADL-LC3	98.96	97.89	97.09	96.96	
mSRC classifier	88.09	85.09	91.66	86.66	
Faster R-CNN	98.66	96.42	97.51	97.20	
DAELGNN	98.61	97.83	96.27	96.68	
RESNET50	93.53	96.01	97.37	96.83	

Fig. 16 Comparative analysis of HIELCC-EDL approach with existing models.

The computation time (CT) outputs of the HIELCC-EDL technique are related with other DL methods in Table 7 and Fig. 17. The outputs depict that the HIELCC-EDL technique attains a minimal CT of 2.19 s. While, the TWSACAE-LCCD, BERTL-HTALCC, MPADL-LC3, mSRC, Faster R-CNN, DAELGNN, and RESNET50 models get increased CT values of 4.52 s, 8.69 s, 3.14 s, 7.95 s, 5.03 s, 3.94 s, and 3.64 s, correspondingly.Table 7 CT outcome of HIELCC-EDL technique with recent models.

Methods	CT (sec)	
HIELCC-EDL	2.19	
TWSACAE-LCCD	4.52	
BERTL-HTALCC	8.69	
MPADL-LC3	3.14	
mSRC Classifier	7.95	
Faster R-CNN	5.03	
DAELGNN	3.94	
RESNET50	3.64	

Fig. 17 CT outcome of HIELCC-EDL technique with recent models.

Therefore, the presented technique can be applied for automated detection and classification of the LCC.

Conclusion

In this article, a novel HIELCC-EDL technique is presented. The presented HIELCC-EDL technique makes use of histopathological images for the detection of LCC. To attain this, the HIELCC-EDL technique involves distinct stages, such as image preprocessing, feature extractor, TSO-based parameter selection, and EL. At the primary stage, the HIELCC-EDL technique uses the WF approach for the noise elimination process. Additionally, the HIELCC-EDL technique utilizes the CA-ResNet50 model for learning complex feature patterns. Moreover, the hyperparameter selection of the CA-ResNet50 model can be carried out using the TSO model. Finally, the detection of LCC takes place using the ensemble of three classifiers such as ELM, CNN, and LSTM. To illustrate the promising performance of the HIELCC-EDL method, a complete set of experimentations has been performed on a standard dataset. The investigational validation of the HIELCC-EDL method depicted a superior accuracy of 99.60% over recent approaches. The limitations of the HIELCC-EDL approach comprises potential challenges in scaling its performance across various datasets beyond histopathological images of LCC. Future studies may explore adapting the HIELCC-EDL approach for handling multi-modal data fusion for overall cancer diagnosis and treatment monitoring and improving its robustness through TL across broader medical imaging domains.

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/43/45. Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R507), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Researchers Supporting Project number (RSPD2024R787), King Saud University, Riyadh, Saudi Arabia.

Author contributions

Conceptualization: Moneerah Alotaibi Data curation and Formal analysis: Amal Alshardan, Alaa O. Khadidos Investigation and Methodology: Moneerah Alotaibi, Mashael Maashi Project administration and Resources: Supervision; Mashael M Asiri Validation and Visualization: Sultan Refa Alotaibi, Raed Alsini Writing—original draft, Moneerah Alotaibi, Ayman Yafoz Writing—review and editing, Mashael M Asiri, Alaa O. Khadidos All authors have read and agreed to the published version of the manuscript.

Data availability

The data supporting this study’s findings are openly available in the Kaggle repository at https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images, reference number23.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Yu G Sun K Xu C Shi X-H Wu C Xie T Meng R-Q Meng X-H Wang K-S Xiao H-M Deng H-W Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images Nat. Commun. 2021 12 1 6311 10.1038/s41467-021-26643-8 34728629
Yu, G. et al. Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images. Nat. Commun. 12(1), 6311 (2021).34728629 10.1038/s41467-021-26643-8
2. Zhuang, Y., Chen, S., Jiang, N. & Hu, H. An effective WSSENet-based similarity retrieval method of large lung CT image databases. KSII Trans. Internet Inf. Syst. 16(7) (2022).
3. Sun, L., Zhang, M., Wang, B. & Tiwari, P. Few-shot class-incremental learning for medical time series classification. IEEE J. Biomed. Health Inform. 1–11 (2023).
4. Wahid, R. R., Nisa, C., Amaliyah, R. P. & Puspaningrum, E. Y. Lung and colon cancer detection with convolutional neural networks on histopathological images. In AIP Conference Proceedings vol. 2654, no. 1 (2023).
5. D. Z. Karim and T. A. Bushra, ‘‘Detecting lung cancer from histopathological images using convolution neural network,’’ in Proceedings of IEEE Region Conference (TENCON) 626–631 (2021).
6. Hatuwal BK Thapa HC ‘Lung cancer detection using convolutional neural network on histopathological images’ Int. J. Comput. Trends Technol. 2020 68 10 21 24 10.14445/22312803/IJCTT-V68I10P104
Hatuwal, B. K. & Thapa, H. C. ‘Lung cancer detection using convolutional neural network on histopathological images’. Int. J. Comput. Trends Technol. 68(10), 21–24 (2020).10.14445/22312803/IJCTT-V68I10P104
7. Mangal, S., Chaurasia, A. & Khajanchi, A.: Convolution neural networks for diagnosing colon and lung cancer histopathological images (2020). http://arxiv.org/abs2009.03878
8. Stephen O Sain M ‘Using deep learning with Bayesian-Gaussian inspired convolutional neural architectural search for cancer recognition and classification from histopathological image frames’ J. Healthc. Eng. 2023 2023 1 9 10.1155/2023/4597445
Stephen, O. & Sain, M. ‘Using deep learning with Bayesian-Gaussian inspired convolutional neural architectural search for cancer recognition and classification from histopathological image frames’. J. Healthc. Eng. 2023, 1–9 (2023).10.1155/2023/4597445
9. Mohalder, R. D., Sarkar, J. P., Hossain, K. A., Paul, L., & Raihan, M. A deep learning based approach to predict lung cancer from histopathological images. In Proceedings of International Conference on Electrical, Computer and Energy Technologies (ICECIT) 1–4 (2021).
10. Xiao X Wang Z Kong Y Lu H Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images Front. Oncol. 2023 13 1081529 10.3389/fonc.2023.1081529 36845699
Xiao, X., Wang, Z., Kong, Y. & Lu, H. Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images. Front. Oncol. 13, 1081529 (2023).36845699 10.3389/fonc.2023.1081529
11. Pacal, I. A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images. Int. J. Mach. Learn. Cybern. 1–19 (2024).
12. Pacal I MaxCerVixT: A novel lightweight vision transformer-based Approach for precise cervical cancer detection Knowl.-Based Syst. 2024 289 111482 10.1016/j.knosys.2024.111482
Pacal, I. MaxCerVixT: A novel lightweight vision transformer-based Approach for precise cervical cancer detection. Knowl.-Based Syst. 289, 111482 (2024).10.1016/j.knosys.2024.111482
13. Pacal, I., Celik, O., Bayram, B. & Cunha, A. Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-based brain tumor classification. Clust. Comput. 1–26 (2024).
14. Kunduracioglu I Pacal I Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases J. Plant Dis. Prot. 2024 131 3 1061 1080 10.1007/s41348-024-00896-z
Kunduracioglu, I. & Pacal, I. Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases. J. Plant Dis. Prot. 131(3), 1061–1080 (2024).10.1007/s41348-024-00896-z
15. De Oliveira CI do Nascimento MZ Roberto GF Tosta TA Martins AS Neves LA Hybrid models for classifying histological images: An association of deep features by transfer learning with ensemble classifier Multimed. Tools Appl. 2024 83 8 21929 21952 10.1007/s11042-023-16351-4
De Oliveira, C. I. et al. Hybrid models for classifying histological images: An association of deep features by transfer learning with ensemble classifier. Multimed. Tools Appl. 83(8), 21929–21952 (2024).10.1007/s11042-023-16351-4
16. Abdullah S Ragab M Tunicate swarm algorithm with deep convolutional neural network-driven colorectal cancer classification from histopathological imaging data Electron. Res. Arch. 2023 31 5 2793 2812 10.3934/era.2023141
Abdullah, S. & Ragab, M. Tunicate swarm algorithm with deep convolutional neural network-driven colorectal cancer classification from histopathological imaging data. Electron. Res. Arch. 31(5), 2793–2812 (2023).10.3934/era.2023141
17. Attallah O Aslan MF Sabanci K A framework for lung and colon cancer diagnosis via lightweight deep learning models and transformation methods Diagnostics 2022 12 12 2926 10.3390/diagnostics12122926 36552933
Attallah, O., Aslan, M. F. & Sabanci, K. A framework for lung and colon cancer diagnosis via lightweight deep learning models and transformation methods. Diagnostics 12(12), 2926 (2022).36552933 10.3390/diagnostics12122926
18. Tenguam JJ Longo LHDC Roberto GF Tosta TA de Faria PR Loyola AM Cardoso SV Silva AB do Nascimento MZ Neves LA Ensemble learning-based solutions: An approach for evaluating multiple features in the context of H&E histological images Appl. Sci. 2024 14 3 1084 10.3390/app14031084
Tenguam, J. J. et al. Ensemble learning-based solutions: An approach for evaluating multiple features in the context of H&E histological images. Appl. Sci. 14(3), 1084 (2024).10.3390/app14031084
19. Muneer, A., Taib, S. M., Hasan, M. H. & Alqushaibi, A. Colorectal cancer recognition using deep learning on histopathology images. In 2023 13th International Conference on Information Technology in Asia (CITA) 25–30 (IEEE, 2023).
20. Mohalder, R. D., Ali, F. B., Paul, L. & Talukder, K. H. Deep learning-based colon cancer tumor prediction using histopathological images. In 2022 25th International Conference on Computer and Information Technology (ICCIT) 629–634 (IEEE, 2022).
21. Sultana, Z., Foysal, M., Islam, S. & Al Foysal, A. Lung cancer detection and classification from chest CT images using an ensemble deep learning approach. In 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT) 364–369 (IEEE, 2024).
22. Alqahtani, H., Alabdulkreem, E., Alotaibi, F., Alnfiai, M. M., Singla, C. & Salama, A. S. Improved water strider algorithm with convolutional autoencoder for lung and colon cancer detection on histopathological images. IEEE Access (2023).
23. https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images
24. Ramos, A. L., Domingo, J. & Barfeh, D. P. Y.. Analysis of weiner filter approximation value based on performance of metrics of image restoration. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) 1–6 (IEEE, 2020).
25. Islam W Jones M Faiz R Sadeghipour N Qiu Y Zheng B Improving performance of breast lesion classification using a ResNet50 model optimized with a novel attention mechanism Tomography 2022 8 5 2411 2425 10.3390/tomography8050200 36287799
Islam, W. et al. Improving performance of breast lesion classification using a ResNet50 model optimized with a novel attention mechanism. Tomography 8(5), 2411–2425 (2022).36287799 10.3390/tomography8050200
26. Zhou Z Zhang H Effatparvar M Improved sports image classification using deep neural network and novel tuna swarm optimization Sci. Rep. 2024 14 1 1 20 38167627
Zhou, Z., Zhang, H. & Effatparvar, M. Improved sports image classification using deep neural network and novel tuna swarm optimization. Sci. Rep. 14(1), 1–20 (2024).38167627
27. Karaman A Karaboga D Pacal I Akay B Basturk A Nalbantoglu U Coskun S Sahin O Hyper-parameter optimization of deep learning architectures using artificial bee colony (ABC) algorithm for high performance real-time automatic colorectal cancer (CRC) polyp detection Appl. Intell. 2023 53 12 15603 15620 10.1007/s10489-022-04299-1
Karaman, A. et al. Hyper-parameter optimization of deep learning architectures using artificial bee colony (ABC) algorithm for high performance real-time automatic colorectal cancer (CRC) polyp detection. Appl. Intell. 53(12), 15603–15620 (2023).10.1007/s10489-022-04299-1
28. Karaman A Pacal I Basturk A Akay B Nalbantoglu U Coskun S Sahin O Karaboga D Robust real-time polyp detection system design based on YOLO algorithms by optimizing activation functions and hyper-parameters with artificial bee colony (ABC) Expert Syst. Appl. 2023 221 119741 10.1016/j.eswa.2023.119741
Karaman, A. et al. Robust real-time polyp detection system design based on YOLO algorithms by optimizing activation functions and hyper-parameters with artificial bee colony (ABC). Expert Syst. Appl. 221, 119741 (2023).10.1016/j.eswa.2023.119741
29. Hui, L. & Chen, J. Contact resistance volatility prediction of pantograph-catenary based on IPOA-ELM. IEEE Access (2024).
30. Abiyev RH Ma’aitaH MKS Deep convolutional neural networks for chest disease detection J. Healthc. Eng. 2018 2018 1 4168538 30154989
Abiyev, R. H. & Ma’aitaH, M. K. S. Deep convolutional neural networks for chest disease detection. J. Healthc. Eng. 2018(1), 4168538 (2018).30154989
31. Lachekhab F Benzaoui M Tadjer SA Bensmaine A Hamma H LSTM-autoencoder deep learning model for anomaly detection in electric motor Energies 2024 17 10 2340 10.3390/en17102340
Lachekhab, F., Benzaoui, M., Tadjer, S. A., Bensmaine, A. & Hamma, H. LSTM-autoencoder deep learning model for anomaly detection in electric motor. Energies 17(10), 2340 (2024).10.3390/en17102340
