Swift Diagnose: A High-Performance Shallow Convolutional Neural Network for Rapid and Reliable SARS-COV-2 Induced Pneumonia Detection





CNN, SARS-COV-2, Healthcare Diagnosis, Covid-19-Related Lung Infection, Lightweight CNN


INTRODUCTION: The SARS-COV-2 pandemic has led to a significant increase in the number of infected individuals and a considerable loss of lives. Identifying SARS-COV-2-induced pneumonia cases promptly is crucial for controlling the virus's spread and improving patient care. In this context, chest X-ray imaging has become an essential tool for detecting pneumonia caused by the novel coronavirus.

OBJECTIVES: The primary goal of this research is to differentiate between pneumonia cases induced specifically by the SARS-COV-2 virus and other types of pneumonia or healthy cases. This distinction is vital for the effective treatment and isolation of affected patients.

METHODS: A streamlined stacked Convolutional Neural Network (CNN) architecture was employed for this study. The dataset, meticulously curated from Johns Hopkins University's medical database, comprised 2292 chest X-ray images. This included 542 images of COVID-19-infected cases and 1266 non-COVID cases for the training phase, and 167 COVID-infected images plus 317 non-COVID images for the testing phase. The CNN's performance was assessed against a well-established CNN model to ensure the reliability of the findings.

RESULTS: The proposed CNN model demonstrated exceptional accuracy, with an overall accuracy rate of 98.96%. In particular, the model achieved a per-class accuracy of 99.405% for detecting SARS-COV-2-infected cases and 98.73% for identifying non-COVID cases. These results indicate the model's significant potential in distinguishing between COVID-19-related pneumonia and other conditions.

CONCLUSION: The research validates the efficacy of using a specialized CNN architecture for the rapid and precise identification of SARS-COV-2-induced pneumonia from chest X-ray images. The high accuracy rates suggest that this method could be a valuable tool in the ongoing fight against the COVID-19 pandemic, aiding in the swift diagnosis and effective treatment of patients.


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Xie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., & Liu, J. (2020). Chest CT for Typi-cal Coronavirus Disease 2019 (SARS-COV-2) Pneumonia: Relationship to Negative RT-PCR Testing. Radiology, 296(2), E41-E45. https://doi.org/10.1148/radiol.2020200343 DOI: https://doi.org/10.1148/radiol.2020200343

Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020). Sensitivity of Chest CT for SARS-COV-2: Comparison to RT-PCR. Radiology, 296(2), E115-E117. https://doi.org/10.1148/radiol.2020200432 DOI: https://doi.org/10.1148/radiol.2020200432

Eastin, C., & Eastin, T. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. The Journal Of Emergency Medicine, 58(4), 711-712. https://doi.org/10.1016/j.jemermed.2020.04.004 DOI: https://doi.org/10.1016/j.jemermed.2020.04.004

Swapnarekha, H., Behera, H., Nayak, J., & Naik, B. (2020). Role of intelligent compu-ting in SARS-COV-2 prognosis: A state-of-the-art review. Chaos, Solitons & Frac-tals, 138, 109947. https://doi.org/10.1016/j.chaos.2020.109947 DOI: https://doi.org/10.1016/j.chaos.2020.109947

Mohamadou, Y., Halidou, A., & Kapen, P. (2020). A review of mathematical modeling, artificial intelligence, and datasets used in the study, prediction, and management of SARS-COV-2. Applied Intelligence, 50(11), 3913-3925. https://doi.org/10.1007/s10489-020-01770-9 DOI: https://doi.org/10.1007/s10489-020-01770-9

Yan, T., Wong, P., Ren, H., Wang, H., Wang, J., & Li, Y. (2020). The automatic distinc-tion between SARS-COV-2 and common pneumonia using multi-scale convolutional neural network on chest CT scans. Chaos, Solitons & Fractals, 140, 110153. https://doi.org/10.1016/j.chaos.2020.110153 DOI: https://doi.org/10.1016/j.chaos.2020.110153

Li, Y., Zhang, Z., Dai, C., Dong, Q., & Badrigilan, S. (2020). Accuracy of deep learning for automated detection of pneumonia using chest X-ray images: A systematic review and meta-analysis. Computers In Biology and Medicine, 123, 103898. https://doi.org/10.1016/j.compbiomed.2020.103898 DOI: https://doi.org/10.1016/j.compbiomed.2020.103898

Elaziz, M., Hosny, K., Salah, A., Darwish, M., Lu, S., & Sahlol, A. (2020). New ma-chine learning method for image-based diagnosis of SARS-COV-2 . PLOS ONE, 15(6), e0235187. https://doi.org/10.1371/journal.pone.0235187 DOI: https://doi.org/10.1371/journal.pone.0235187

Khandual, A., Dutta, K., Lenka, R., Nayak, S., & Bhoi, A. (2021). MED-NET: a novel approach to ECG anomaly detection using LSTM auto-encoders. International Journal Of Computer Applications In Technology, 65(4), 343. https://doi.org/10.1504/ijcat.2021.10040403 DOI: https://doi.org/10.1504/IJCAT.2021.10040403

Gupta, P., Saxena, N., Sharma, M., & Tripathi, J. (2018). Deep Neural Network for Human Face Recognition. International Journal Of Engineering And Manufactur-ing, 8(1), 63-71. https://doi.org/10.5815/ijem.2018.01.06 DOI: https://doi.org/10.5815/ijem.2018.01.06

Lenka, R., Dutta, K., Khandual, A., & Nayak, S. (2020). Bio-Medical Image Pro-cessing. Examining Fractal Image Processing And Analysis, 158-169. https://doi.org/10.4018/978-1-7998-0066-8.ch007 DOI: https://doi.org/10.4018/978-1-7998-0066-8.ch007

Cheng, J., Ni, D., Chou, Y., Qin, J., Tiu, C., & Chang, Y. et al. (2016). Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans. Scientific Reports, 6(1). https://doi.org/10.1038/srep24454 DOI: https://doi.org/10.1038/srep24454

Reid, S., Tibshirani, R., & Friedman, J. (2016). A study of error variance estimation in Lasso regression. Statistica Sinica. https://doi.org/10.5705/ss.2014.042 DOI: https://doi.org/10.5705/ss.2014.042

Hara, K., Saito, D., & Shouno, H. (2015). Analysis of function of rectified linear unit used in deep learning. 2015 International Joint Conference On Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2015.7280578 DOI: https://doi.org/10.1109/IJCNN.2015.7280578

Dubey, A.K. and Jain, V. (2019). Comparative Study of Convolution Neural Network's ReLu and Leaky-ReLu Activation Functions. In Applications of Computing, Automation and Wireless Systems in Electrical Engineering Springer, pp. 873-880. https://arxiv.org/pdf/2109.14545.pdf DOI: https://doi.org/10.1007/978-981-13-6772-4_76

Wanto, A., Windarto, A., Hartama, D., & Parlina, I. (2017). Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density. IJISTECH (International Journal Of Information System & Technology), 1(1), 43. https://doi.org/10.30645/ijistech.v1i1.6 DOI: https://doi.org/10.30645/ijistech.v1i1.6

Ramos, D., Franco-Pedroso, J., Lozano-Diez, A., & Gonzalez-Rodriguez, J. (2018). Deconstructing Cross-Entropy for Probabilistic Binary Classifiers. Entropy, 20(3), 208. https://doi.org/10.3390/e20030208 DOI: https://doi.org/10.3390/e20030208

Diederik, P., Kingma and Jimmy Lei Ba. (2015). Adam: A Method for Stochastic Op-timization. International Conference on Learning Representations, pp. 1-13. https://moodle2.cs.huji.ac.il/nu15/pluginfile.php/316969/mod_resource/content/1/adam_pres.pdf

Boltzmann, L. (1974). The Second Law of Thermodynamics. Theoretical Physics And Philosophical Problems, 13-32. https://doi.org/10.1007/978-94-010-2091-6_2 DOI: https://doi.org/10.1007/978-94-010-2091-6_2

Rowlinson *, J. (2005). The Maxwell–Boltzmann distribution. Molecular Phys-ics, 103(21-23), 2821-2828. https://doi.org/10.1080/002068970500044749 DOI: https://doi.org/10.1080/002068970500044749

Luque, A., Carrasco, A., Martín, A., & de las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion ma-trix. Pattern Recognition, 91, 216-231. https://doi.org/10.1016/j.patcog.2019.02.023 DOI: https://doi.org/10.1016/j.patcog.2019.02.023

Goutte, C., & Gaussier, E. (2005). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. Lecture Notes In Computer Science, 345-359. https://doi.org/10.1007/978-3-540-31865-1_25 DOI: https://doi.org/10.1007/978-3-540-31865-1_25

Kumar, A., Sarkar, S., & Pradhan, C. (2019). Malaria Disease Detection Using CNN Technique with SGD, RMSprop and ADAM Optimizers. Studies In Big Data, 211-230. https://doi.org/10.1007/978-3-030-33966-1_11 DOI: https://doi.org/10.1007/978-3-030-33966-1_11

Huk, M. (2020). Stochastic Optimization of Contextual Neural Networks with RMSprop. In Asian Conference on Intelligent Information and Database Systems Springer, Cham pp. 343-352. DOI: https://doi.org/10.1007/978-3-030-42058-1_29

Chouhan, V., Singh, S., Khamparia, A., Gupta, D., Tiwari, P., & Moreira, C. et al. (2020). A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images. Applied Sciences, 10(2), 559. https://doi.org/10.3390/app10020559 DOI: https://doi.org/10.3390/app10020559

Satpathy, S., Mangla, M., Sharma, N., Deshmukh, H., & Mohanty, S. (2021). Predicting mortality rate and associated risks in SARS-COV-2 patients. Spatial Information Re-search, 29(4), 455-464. https://doi.org/10.1007/s41324-021-00379-5 DOI: https://doi.org/10.1007/s41324-021-00379-5

Khadidos, A., Khadidos, A., Kannan, S., Natarajan, Y., Mohanty, S., & Tsaramirsis, G. (2020). Analysis of SARS-COV-2 Infections on a CT Image Using DeepSense Mod-el. Frontiers In Public Health, 8. https://doi.org/10.3389/fpubh.2020.599550 DOI: https://doi.org/10.3389/fpubh.2020.599550

Dutta, K., & Gupta, P. (2022, December). Reso-Net: Generic Image Resolution En-hancement Using Convolutional Autoencoders. In International Conference on Intelli-gent Systems and Machine Learning (pp. 298-308). Cham: Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-35078-8_25

Sah S, Surendiran B, Dhanalakshmi R, Yamin M. SARS-CoV-2 cases prediction using SARIMAX Model by tuning hyperparameter through grid search cross-validation ap-proach. Expert Syst. 2022 Jul 15:e13086. doi: 10.1111/exsy.13086. DOI: https://doi.org/10.1111/exsy.13086

Shankar, K., Mohanty, S.N., Yadav, K. et al. Automated SARS-COV-2 diagnosis and classification using convolutional neural network with fusion based feature extraction model. Cogn Neurodyn (2021). https://doi.org/10.1007/s11571-021-09712-y DOI: https://doi.org/10.1007/s11571-021-09712-y

Sah, S., Surendiran, B., Dhanalakshmi, R., Mohanty, S. N., Alenezi, F., & Polat, K. (2022). Forecasting SARS-COV-2 Pandemic Using Prophet, ARIMA, and Hybrid Stacked LSTM-GRU Models in India. Computational and Mathematical Methods in Medicine, 2022, 1–19. https://doi.org/10.1155/2022/1556025 DOI: https://doi.org/10.1155/2022/1556025

Shome D, Kar T, Mohanty SN, Tiwari P, Muhammad K, AlTameem A, Zhang Y, Saudagar AKJ. COVID-Transformer: Interpretable SARS-COV-2 Detection Using Vi-sion Transformer for Healthcare. Int J Environ Res Public Health. 2021 Oct 21;18(21):11086. doi: 10.3390/ijerph182111086. DOI: https://doi.org/10.3390/ijerph182111086

Gupta M., Jain R., Taneja S., Chaudhary G., Khari M. and Verdú E., Real-time meas-urement of the uncertain epidemiological appearances of SARS-COV-2 infections, Ap-plied Soft Computing, vol.101, pp.107039, 2021. DOI: https://doi.org/10.1016/j.asoc.2020.107039

Rajagopal A., Joshi G.P., Ramachandran R., Subhalakshmi R. T., Khari M., Jha S, and You J., A deep learning model based on multi-objective particle swarm optimization for scene classification in unmanned aerial vehicles, IEEE Access, vol.8, pp.135383-135393, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3011502

Yousef, R., Gupta, G., Yousef, N. and Khari, M., A holistic overview of deep learning approach in medical imaging, Multimedia Systems, vol.28, Issue.3, pp.881-914, 2022. DOI: https://doi.org/10.1007/s00530-021-00884-5

Adil Khadidos, A Robust and Computationally Faser Approach to SARS-COV-2 Diagnosis using Shallow Convolutional Neural Architecture, Paper ID: 12A13Q Volume 12 Issue 13




How to Cite

Dutta K, Lenka R, Gupta P, Goel A, Naga Ramesh JV. Swift Diagnose: A High-Performance Shallow Convolutional Neural Network for Rapid and Reliable SARS-COV-2 Induced Pneumonia Detection. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 28 [cited 2024 Apr. 21];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5581

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