Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study. (February 2021)

• Record Type: Journal Article
• Authors: Nayak, Soumya Ranjan; Nayak, Deepak Ranjan; Sinha, Utkarsh; Arora, Vaibhav; Pachori, Ram Bilas
• Is Part Of: Biomedical signal processing and control. Volume 64(2021)
• Publication Date: 2021-02
• Subjects: COVID-19 -- SARS-CoV-2 -- Optimization algorithms -- Convolutional Neural Networks -- Chest X-ray; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102365
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: The emergence of Coronavirus Disease 2019 (COVID-19) in early December 2019 has caused immense damage to health and global well-being. Currently, there are approximately five million confirmed cases and the novel virus is still spreading rapidly all over the world. Many hospitals across the globe are not yet equipped with an adequate amount of testing kits and the manual Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test is time-consuming and troublesome. It is hence very important to design an automated and early diagnosis system which can provide fast decision and greatly reduce the diagnosis error. The chest X-ray images along with emerging Artificial Intelligence (AI) methodologies, in particular Deep Learning (DL) algorithms have recently become a worthy choice for early COVID-19 screening. This paper proposes a DL assisted automated method using X-ray images for early diagnosis of COVID-19 infection. We evaluate the effectiveness of eight pre-trained Convolutional Neural Network (CNN) models such as AlexNet, VGG-16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, ResNet-50 and Inception-V3 for classification of COVID-19 from normal cases. Also, comparative analyses have been made among these models by considering several important factors such as batch size, learning rate, number of epochs, and type of optimizers with an aim to find the best suited model. The models have been validated on publicly available chest X-ray images and the best performance is obtained by ResNet-34 with an accuracy of 98.33%. This study will be useful for researchers to think for the design of more effective CNN based models for early COVID-19 detection. Highlights: A deep learning assisted automated method is proposed for COVID-19 diagnosis. A comprehensive study among eight pre-trained CNN models is performed. The impact of several hyper-parameters have been analyzed. The best performing model is obtained and compared with the state-of-the-art methods. The model could assist radiologists for accurate and stable COVID-19 diagnosis.