==== Front Front Public Health Front Public Health Front. Public Health Frontiers in Public Health 2296-2565 Frontiers Media S.A. 10.3389/fpubh.2020.599550 Public Health Original Research Analysis of COVID-19 Infections on a CT Image Using DeepSense Model Khadidos Adil 1 Khadidos Alaa O. 2 Kannan Srihari 3* Natarajan Yuvaraj 4 Mohanty Sachi Nandan 5 Tsaramirsis Georgios 6 1Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia 2Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia 3Department of Computer Science and Engineering, SNS College of Engineering, Coimbatore, India 4Research and Development, Information Communication Technology Academy, Chennai, India 5Department of Computer Science and Engineering, Institute of Chartered Financial Analysts of India Foundation of Higher Education, Hyderabad, India 6Higher Colleges of Technology, Women's College, Abu Dhabi, United Arab Emirates Edited by: Deepak Gupta, Maharaja Agrasen Institute of Technology, India Reviewed by: Abdulsattar Abdullah Hamad, University of Tikrit, Iraq; George Halikias, City University of London, United Kingdom; Mohammad Yamin, King Abdulaziz University, Saudi Arabia *Correspondence: Srihari Kannan harionto@gmail.comThis article was submitted to Digital Public Health, a section of the journal Frontiers in Public Health 20 11 2020 2020 20 11 2020 8 59955027 8 2020 16 10 2020 Copyright © 2020 Khadidos, Khadidos, Kannan, Natarajan, Mohanty and Tsaramirsis.2020Khadidos, Khadidos, Kannan, Natarajan, Mohanty and TsaramirsisThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.In this paper, a data mining model on a hybrid deep learning framework is designed to diagnose the medical conditions of patients infected with the coronavirus disease 2019 (COVID-19) virus. The hybrid deep learning model is designed as a combination of convolutional neural network (CNN) and recurrent neural network (RNN) and named as DeepSense method. It is designed as a series of layers to extract and classify the related features of COVID-19 infections from the lungs. The computerized tomography image is used as an input data, and hence, the classifier is designed to ease the process of classification on learning the multidimensional input data using the Expert Hidden layers. The validation of the model is conducted against the medical image datasets to predict the infections using deep learning classifiers. The results show that the DeepSense classifier offers accuracy in an improved manner than the conventional deep and machine learning classifiers. The proposed method is validated against three different datasets, where the training data are compared with 70%, 80%, and 90% training data. It specifically provides the quality of the diagnostic method adopted for the prediction of COVID-19 infections in a patient. DeepSenseartificial intelligenceconvolutional neural networkCT imagespredictionCOVID-19 ==== Body Introduction The novel coronavirus disease 2019 (COVID-19) is a pandemic outbreak (1). COVID-19 patients are classified essentially based on computerized tomography (CT) lung images, and it is used widely for testing. The healthcare institutions fitted with CT scans help in the process of image acquisition and classification of CT images at a faster rate. However, the need for an expert medical practitioner is hence required for the verification of the final results, which increases the time of computation (2). On the other hand, the supervised learning models (3–10) can be utilized for classifying the patients from the CT images. Infections based on CT images are not classified using very little unattended methods (11–24). We have developed a model that mainly includes supervised and unsupervised learning models in order to improve the classification process. The aim is to classify the infected patients automatically based on their CT images. In this paper, a DeepSense algorithm is utilized to diagnose COVID-19 infections among the medical community. The deep learning method is designed as a combination of convolutional neural network (CNN) and recurrent neural network (RNN) that reduces the classifier burden on optimal classification of the multidimensional data features. The main contribution of the work includes the following: (a) The authors develop a combined CNN and RNN to classify the medical image datasets. (b) The experimental results are conducted to measure the correctness in terms of its accuracy, precision, and recall values against artificial neural network (ANN), feedforward neural network (FFNN), back propagation neural network (BPNN), deep neural network (DNN), and RNN. The outline of the paper is presented as follows: The Methods section provides the details of the ensemble classifier. The DeepSense Model section evaluates the entire work. The Results and Discussions section concludes the work with future enhancement. Methods The deep learning model namely DeepSense algorithm is a combination of CNN and RNN designed to improve the performance of the classification accuracy. DeepSense learning is regarded as a module for accurate predictions of lung infections caused by the COVID-19 virus. Figure 1 shows the architecture of the proposed classification model using the DeepSense algorithm. Figure 1 Proposed model for classification. Deepsense Model Figure 2 shows the DeepSense DNN (25) model that has three components, including convolutional, recurrent, and output layer that are stacked upon one another. The convolutional and recurrent layers are regarded as the significant building blocks (Figure 1), and the output layer is considered as a specific layer that classifies the images. The DeepSense DNN model is designed for the classification of input CT images for COVID-19-related infections. Figure 2 Proposed DeepSense deep neural network (DNN) architecture. The DeepSense network avoids gradient exploding and improves the rate of convergence using residual learning, adjustable learning rate, and gradient clipping that helps in optimizing the process of training. The features are extracted through the DeepSense model that increases the reconstruction accuracy and reduces the time of training. Such optimization helps in obtaining the rich text information, and it has better ability for classification. Convolutional Layers The convolutional layers have three different parts that include individual convolutional subnets for input from CT device X(k), where k is the number of CT device. The other subnets include a merged convolutional subnet for K convolutional subnets' outputs. For a time interval t, the matrix X(k) is used as an input to the DNN architecture that extracts the relationship of X(k,t), which includes the relationships lying inside the frequency domain. The sensor measurement interactions include entire dimension, where the frequency domain usually has several local patterns. These interactions are studied using 2D filters and produces the output X(k,1,t) based on the local patterns and dimensions in frequency domain. The high-level relationships are learned hierarchically using the application of a 1D filter. The matrix is then flattened into a vector, and they are concatenated to produce the input for RNN layers. The activation function in the convolutional layer is a rectified linear unit (ReLU) function, and batch normalization eliminates the internal covariate shift. Recurrent Layers The RNN architecture learns the needed features having long-term dependencies (long paths). The study uses Gated Recurrent Unit (GRU) on long and short path selection to reduce well the network complexity. A set of three layers stacked in GRU is used in this paper that uses time flow that runs the stacked GRU incrementally for faster input data processing. The recurrent layer outputs vector series {x(r,t)} where t = 1,2,···, T for the process of classification at the output layer. Output Layer For the purpose of classification, {x(r,t)} is selected as the feature vector, and this layer converts the vector of variable length into fixed length. The final feature is generated by averaging the features over a specific time interval based on long or short paths, x(r) x(r)=∑t=1Tx(t,r)T. Finally the probability of predicted category is generated by feeding the averaging features into the softmax layer. Type-Specific Layer For the customization of the DeepSense layer to operate the process of classification, we specifically use the following process: Step 1: Identify the input image Step 2: Preprocessing input image for temporal and spectral noise Step 3: Extract the features related to COVID-19 infections Step 4: Apply DeepSense classifier for optimal classifier. Results and Discussions This section provides the results of comparison between the machine/deep learning classifiers for predicting COVID-19 infections using IEEE8023 (26), COVID-CT-Dataset (27), and COVID-19 Open Research Dataset Challenge (CORD-19) (28) datasets. IEEE8023 has the image collection from various sources including COVID-19 or viral and bacterial pneumonias in the form of CT images. COVID-CT has 349 COVID-19 CT images from 216 patients and 463 non-COVID-19 CTs. CORD-19 has collected the CT image resources from 52,000 scholarly articles. The study is experimented using a 10-fold cross validation, which is tested with all these three base classifiers. Experiment The performance measures for evaluating the DeepSense classifier is estimated against various metrics: accuracy, geometric mean (G-mean), F-measure, precision, percentage error, specificity, and sensitivity. Accuracy for optimal classification is given below: (1) Accuracy=TP+TNTP+TN+FP+FN where: TP is defined as the true positive TN is defined as the true negative FP is defined as the false positive FN is defined as the false negative F-measure of the DeepSense classifier is defined as follows: (2) F-measure=2TP2TP+FP+FN G-mean of the DeepSense classifier is defined as follows: (3) G-mean=TPTP+FN×TNTN+FP Mean absolute percentage error (MAPE) of the DeepSense classifier is defined as follows: (4) MAPE=100n∑t=1n|At-FtAt| Where At is defined as the actual class Ft is defined as the predicted class, and n is defined as the fitted points Sensitivity of the DeepSense is defined as: (5) Sensitivity=TPTP+FN Specificity of the DeepSense is defined as: (6) Specificity=TNTN+FP Analysis In this section, we provide the results of various meta-ensemble classifiers that include FFNN (29), ANN (25), DNN (30), BPNN (31), and RNN (32). The proposed method is validated against three different datasets, where the training data are compared with 70% (Figure 3), 80% (Figure 4), and 90% (Figure 5) training data. Figure 3 Results of classification accuracy during training with 70% training data. Figure 4 Results of classification accuracy during training with 80% training data. Figure 5 Results of classification accuracy during training with 90% training data. Figure 3 shows the results of classification accuracy of CORD-19 datasets for all residuals are higher, and with increasing residuals, the accuracy increases. Same is the case for other training sets; however, with 80% datasets, the accuracy is fluctuating due to the extraction of on-optimal features from IEEE8023 datasets. Tables 1, 4, 7 provide the results of statistical parameters on predicting COVID-19 infections over 70, 80, and 90% training data over IEEE8023 datasets. Table 1 Results of statistical parameters for IEEE8023 with 70% training data on 1,000 images. Statistical parameters ANN FFNN BPNN DNN RNN DeepSense Accuracy 55.67145 55.97152 58.06198 58.32304 59.68335 80.475 F-measure 38.39159 40.49205 51.72857 51.8886 54.26013 83.65671 G-mean 72.54022 72.77127 74.27161 74.31162 74.72171 85.57814 MAPE 28.32533 25.38368 23.98336 21.40179 20.82166 16.1186 Sensitivity 61.74481 65.25659 73.16136 85.54813 86.20828 96.25452 Specificity 74.18159 74.37163 77.88342 77.90342 79.27473 80.11492 ANN, artificial neural network; BPNN, back propagation neural network; DNN, deep neural network; FFNN, feedforward neural network; MAPE, mean absolute percentage error; RNN, recurrent neural network. Tables 2, 5, 8 provide the results of statistical parameters on predicting COVID-19 infections over 70, 80, and 90% training data over COVID-CT datasets. Table 2 Results of statistical parameters for COVID-CT with 70% training data on 1,000 images. Statistical parameters ANN FFNN BPNN DNN RNN DeepSense Accuracy 56.26158 58.87317 61.23469 62.605 65.84672 84.68794 F-measure 66.74693 66.79694 67.68814 68.80839 73.84151 79.40476 G-mean 43.50373 56.43162 59.4633 44.705 76.09302 85.98823 MAPE 19.37033 16.69873 16.60871 11.75563 10.42533 9.275074 Sensitivity 76.23305 78.91465 79.00467 83.84675 85.18805 86.33831 Specificity 73.39141 76.27306 77.21327 80.37497 82.37642 84.46789 ANN, artificial neural network; BPNN, back propagation neural network; COVID-19, coronavirus disease 2019; DNN, deep neural network; FFNN, feedforward neural network; MAPE, mean absolute percentage error; RNN, recurrent neural network. Tables 3, 6, 9 provide the results of statistical parameters on predicting COVID-19 infections over 70, 80, and 90% training data over CORD-19 datasets. Table 3 Results of statistical parameters for CORD-19 with 70% training data on 1,000 images. Statistical parameters ANN FFNN BPNN DNN RNN DeepSense Accuracy 59.07321 65.85673 68.8784 74.09157 77.93343 82.41643 F-measure 69.68858 69.93964 70.10968 70.28972 74.85174 80.39498 G-mean 69.98965 70.2197 71.94009 74.00155 76.4631 79.21471 MAPE 68.11823 64.47642 57.75191 39.63186 36.77022 34.91881 Sensitivity 77.52334 71.14991 71.87007 73.64147 73.84151 80.69505 Specificity 70.39974 72.28016 75.35185 80.58502 81.89631 82.30641 ANN, artificial neural network; BPNN, back propagation neural network; CORD-19, COVID-19 Open Research Dataset Challenge; COVID-19, coronavirus disease 2019; DNN, deep neural network; FFNN, feedforward neural network; MAPE, mean absolute percentage error; RNN, recurrent neural network. Table 4 Results of statistical parameters for IEEE8023 with 80% training data on 1,000 images. Statistical parameters ANN FFNN BPNN DNN RNN DeepSense Accuracy 96.46457 97.18473 97.21474 97.29476 97.30476 97.43479 F-measure 52.36871 69.72859 70.04966 72.93131 76.16303 79.36475 G-mean 81.88631 82.7365 84.35786 85.91821 90.96134 92.48168 MAPE 26.90502 25.51371 22.74209 20.07049 10.60537 90.12115 Sensitivity 68.69836 70.08967 72.86129 75.54189 84.99801 88.59981 Specificity 96.53459 97.32476 97.52481 97.60483 97.62483 97.68484 ANN, artificial neural network; BPNN, back propagation neural network; DNN, deep neural network; FFNN, feedforward neural network; MAPE, mean absolute percentage error; RNN, recurrent neural network. Table 5 Results of statistical parameters for COVID-CT with 80% training data on 1,000 images. Statistical parameters ANN FFNN BPNN DNN RNN DeepSense Accuracy 97.73486 97.75486 97.77486 97.77486 97.78487 97.78487 F-measure 89.19995 90.63127 90.7813 91.29141 91.50146 92.1416 G-mean 93.27286 96.67462 97.22474 97.52481 97.66484 97.66484 MAPE 86.64838 27.01504 20.25053 9.275074 54.63022 21.0017 Sensitivity 88.94989 95.58337 96.68462 97.26475 97.54481 97.55482 Specificity 96.76464 96.78464 96.78464 96.78464 96.78464 97.42479 ANN, artificial neural network; BPNN, back propagation neural network; COVID-19, coronavirus disease 2019; DNN, deep neural network; FFNN, feedforward neural network; MAPE, mean absolute percentage error; RNN, recurrent neural network. Table 6 Results of statistical parameters for CORD-19 with 80% training data on 1,000 images. Statistical parameters ANN FFNN BPNN DNN RNN DeepSense Accuracy 93.97301 94.12305 94.20307 94.25308 94.43312 94.44312 F-measure 58.25303 60.04343 60.3835 60.84361 62.37495 62.51498 G-mean 79.1447 79.65481 80.18493 80.41498 81.52623 81.98633 MAPE 29.90669 29.17552 28.28533 27.93525 26.10384 25.28365 Sensitivity 65.69669 66.42685 67.31805 67.67813 69.49854 70.32973 Specificity 95.2533 95.41334 95.42334 95.47335 95.51336 95.56337 ANN, artificial neural network; BPNN, back propagation neural network; CORD-19, COVID-19 Open Research Dataset Challenge; COVID-19, coronavirus disease 2019; DNN, deep neural network; FFNN, feedforward neural network; MAPE, mean absolute percentage error; RNN, recurrent neural network. Table 7 Results of statistical parameters for IEEE8023 with 90% training data on 1,000 images. Statistical parameters ANN FFNN BPNN DNN RNN DeepSense Accuracy 95.91445 95.93445 95.94446 96.03448 96.05448 96.11449 F-measure 77.3833 77.51333 78.03345 79.11469 79.79484 80.08491 G-mean 79.43476 79.67482 79.94488 80.9451 81.26517 81.45622 MAPE 31.14697 30.78688 30.26677 28.65541 28.1553 27.83522 Sensitivity 64.45641 64.81649 65.33661 66.94797 67.44808 67.76815 Specificity 94.72318 94.7832 94.8232 96.05448 96.46457 96.81465 ANN, artificial neural network; BPNN, back propagation neural network; DNN, deep neural network; FFNN, feedforward neural network; MAPE, mean absolute percentage error; RNN, recurrent neural network. Table 8 Results of statistical parameters for COVID-CT with 90% training data on 1,000 images classifier. Statistical parameters ANN FFNN BPNN DNN RNN DeepSense Accuracy 97.37478 97.37478 97.45479 97.45479 97.4748 97.52481 F-measure 85.88821 86.00823 87.94967 87.97967 89.33998 89.34998 G-mean 94.03303 94.03303 94.40311 94.47313 94.8032 94.84321 MAPE 70.79983 70.61979 62.72503 61.39473 54.04008 53.35993 Sensitivity 90.53124 90.53124 91.34143 91.47146 92.21162 92.28164 Specificity 97.4648 97.4748 97.56482 97.56482 97.65484 97.65484 ANN, artificial neural network; BPNN, back propagation neural network; COVID-19, coronavirus disease 2019; DNN, deep neural network; FFNN, feedforward neural network; MAPE, mean absolute percentage error; RNN, recurrent neural network. Table 9 Results of statistical parameters for CORD-19 with 90% training data on 1,000 images. Statistical parameters ANN FFNN BPNN DNN RNN DeepSense Accuracy 97.44379 97.44379 97.52381 97.52381 97.54381 97.59382 F-measure 85.94922 86.06925 88.01168 88.04169 89.40299 89.41399 G-mean 94.09904 94.09904 94.47013 94.54014 94.87022 94.91022 MAPE 70.84984 70.6698 62.77004 61.43874 54.07909 53.39794 Sensitivity 90.59526 90.59526 91.40644 91.53647 92.27664 92.34765 Specificity 97.53381 97.54381 97.63383 97.63383 97.72385 97.72385 ANN, artificial neural network; BPNN, back propagation neural network; CORD-19, COVID-19 Open Research Dataset Challenge; COVID-19, coronavirus disease 2019; DNN, deep neural network; FFNN, feedforward neural network; MAPE, mean absolute percentage error; RNN, recurrent neural network. Evaluation Criteria The simulation results show that the DeepSense classifier has higher classification accuracy than the existing meta-ensemble classifiers. In addition, the CORD-19 datasets offer optimum selection of features to increase classification accuracy by 90% training data over 80 or 90%. The other measurements are optimal for CORD-19 than the other selection tools. Furthermore, MAPE is less than the other methods in the deep learning model. The result shows that the CORD-19 datasets are more accurate than RNN and DNN. The results also show that the classification accuracy with IEEE8023 as a functional selection tool decreases at some point as the number of residues increases compared to COVID-CT and CORD-19. The class of infections is therefore accurately determined with the proposed classification. Conclusions and Future Work In this paper, a DeepSense algorithm is designed for the classification of COVID-19 infections. The DeepSense algorithm helps in optimal classification of multidimensional features from CT images. The classifier combined with hybrid deep learning classifier, namely, CNN and RNN, helps in improving the prediction of events from a medical image. The extraction of optimal features from the feature extraction model helps the classifier to optimally detect whether the patient is infected or not. The experimental results show that the proposed method has higher accuracy than the other methods. In the future, the model can be designed with an ensemble data model to classify the highly rated multidimensional dataset. Data Availability Statement The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author/s. Author Contributions AK: visualization and investigation. AOK: data curation, software, and validation. SK: methodology, data curation, review and editing, and supervision. YN: conceptualization, methodology, writing original draft, software, and data curation. 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