Prateek CHHIKARA, Prakhar GUPTA, Prabhjot SINGH, Tarunpreet BHATIA

A deep transfer learning based model for automatic detection of COVID-19 from chest X-rays

Deep learning in medical imaging has revolutionized the way we interpret medical data, as high computational devices’ capabilities are far more than their creators. With the pandemic causing havoc for the second straight year, the findings in our paper will allow researchers worldwide to use and create state-of-the-art models to detect affected persons before it reaches the R number. The paper proposes an automated diagnostic tool using the deep learning models on chest x-rays as an input to reach a point where we surpass this pandemic (COVID-19 disease). A deep transfer learning-based model for automatic detection of COVID-19 from chest x-rays using the Inception-V3 model is proposed, in which we added flattening, node dropping, normalization, and dense layer. The proposed architecture is compared with existing state-of-the-art ImageNet models. The model’s efficacy is tested on three different COVID-19 radiography datasets with three classes: COVID, normal, and viral pneumonia. The proposed model has reached an accuracy of 97.7%, 84.95%, and 97.03% on the mentioned datasets, respectively. The proposed work introduces the deep neural networks applied to medical images to analyze image enhancement techniques and emphasize the field’s clinical aspects.

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[1] Barnawi A, Chhikara P, Tekchandani R, Kumar N, Alzahrani B. Artificial intelligence-enabled Internet of Thingsbased system for COVID-19 screening using aerial thermal imaging. Future Generation Computer Systems 2021; 124: 119-132. doi: 10.1016/j.future.2021.05.019
[2] Fang Y, Zhang H, Xie J, Lin M, Ying L et al. Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 2020; 296 (2): E115–E117. doi: 10.1148/radiol.2020200432
[3] Singh D, Kumar V, Kaur M. Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks. European Journal of Clinical Microbiology & Infectious Diseases 2020; 39 (7): 1379–1389. doi: 10.1007/s10096-020-03901-z
[4] Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning 2017. https://arxiv.org/abs/1711.05225
[5] Tartaglione E, Barbano C, Berzovini C, Calandri M, Grangetto M. Unveiling covid-19 from chest x-ray with deep learning: a hurdles race with small data. International Journal of Environmental Research and Public Health 2020; 17 (18): 6933. doi: 10.3390/ijerph17186933
[6] Asnaoui K, Chawki Y. Using X-ray images and deep learning for automated detection of coronavirus disease. Journal of Biomolecular Structure and Dynamics 2020; 1–12. doi: 10.1080/07391102.2020.1767212
[7] Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Applied Intelligence 2021; 51: 854–864. doi: 10.1007/s10489-020-01829-7
[8] Heidari M, Mirniaharikandehei S, Khuzani A, Danala G, Qui Y et al. Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. International Journal of Medical Informatics 2020; 144: doi: 10.1016/j.ijmedinf.2020.104284
[9] Siddhartha M, Santra A. COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19 2021. https://arxiv.org/abs/2006.13873
[10] Siracusano G, Corte A, Gaeta M, Cicero G, Chiappini M et al. Pipeline for Advanced Contrast Enhancement (PACE) of chest X-ray in evaluating COVID-19 patients by combining bidimensional empirical mode decomposition and CLAHE. Sustainability 2020; 12 (20): 8573. doi: 10.3390/su12208573
[11] Das D, Santosh K, Pal U. Truncated inception net: COVID-19 outbreak screening using chest X-rays. Physical and engineering sciences in medicine 2020; 43: 915–925. doi: 10.1007/s13246-020-00888-x
[12] Ozturk T, Talo M, Yildirim E, Baloglu U, Yildirim O, et al. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine 2020; 121: doi: 10.1016/j.compbiomed.2020.103792
[13] Khan A, Shah J, Bhat M. Coronet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine 2020. 196: doi: 10.1016/j.cmpb.2020.105581
[14] Apostolopoulos I.D., Aznaouridis S.I., Tzani M.A. Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases. Journal of Medical and Biological Engineering 2020; 40:462–469. doi: 10.1007/s40846-020-00529-4
[15] Ucar F, Korkmaz D. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based Diagnostic of the Coronavirus Disease 2019 (COVID-19) from X-Ray Images. Medical Hypotheses 2020; 140: doi: 10.1016/j.mehy.2020.109761
[16] Loey M, Smarandache F, Khalifa N. Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning. Symmetry 2020; 12 (4): 651. doi: 10.3390/sym12040651
[17] Luz E, Silva P, Silva R, Silva L, Guimaraes J et al. Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images. Research on Biomedical Engineering 2021. doi: 10.1007/s42600- 021-00151-6
[18] Shan F, Gao Y, Wang J, Shi W, Shi N et al. Lung infection quantification of covid-19 in ct images with deep learning 2020. https://arxiv.org/pdf/2003.04655.pdf
[19] Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Analysis and Applications 2021. doi: 10.1007/s10044-021-00984-y
[20] Huang L, Han R, Ai T, Yu P, Kang H et al. Serial quantitative chest CT assessment of COVID-19: a deep learning approach, Radiology: Cardiothoracic Imaging 2020; 2 (2). doi: 10.1148/ryct.2020200075
[21] Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine 2020; 43: 635–640. doi: 10.1007/s13246-020-00865-4
[22] Maia M, Pimentel J, Pereira I, Gondim J, Barreto M et al. Convolutional Support Vector Models: Prediction of Coronavirus Disease Using Chest X-rays. Information; 11 (12): 548. doi: 10.3390/info11120548
[23] Bai X, Fang C, Zhou Y, Bai S, Liu Z et al. Predicting COVID-19 malignant progression with AI techniques. SSRN 2020; 1–29. doi: 10.2139/ssrn.3557984
[24] Sun L, Song F, Shi N, Liu F, Li S et al. Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19. Journal of Clinical Virology 2020; 128. doi: 10.1016/j.jcv.2020.104431
[25] Ibrahim A, Ozsoz M, Serte S, Al-Turjman F, Yakoi P. Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19. Cognitive Computation 2021. 1–13. doi: 10.1007/s12559-020-09787-5
[26] Ghoshal B, Tucker A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection 2020. https://arxiv.org/abs/2003.10769
[27] Chhikara P, Singh P, Gupta P, Bhatia T. Deep Convolutional Neural Network with Transfer Learning for Detecting Pneumonia on Chest X-Rays. In: Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals 2020; 155–168. doi: 10.1007/978-981-15-0339-9_13
[28] Liu H. Feature extraction and image recognition with convolutional neural networks. Journal of Physics: Conference Series 2018; 1087 (6). doi: 10.1088/1742-6596/1087/6/062032
[29] Meyer-Baese A, Schmid V. In: Chapter 4 - The Wavelet Transform in Medical Imaging. Pattern Recognition and Signal Analysis in Medical Imaging (Second Edition) 2014. 113–134. doi: 10.1016/B978-0-12-409545-8.00004-2
[30] Kurt B, Nabiyev V, Turhan K. Medical images enhancement by using anisotropic filter and clahe. In: 2012 International Symposium on Innovations in Intelligent Systems and Applications 2012. pp. 1–4. doi: 10.1109/INISTA.2012.6246971
[31] Chen G, Chen P, Shi Y, Hsieh C, Liao B et al. Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks 2019.https://arxiv.org/abs/1905.05928