ABSTRACT
Coronavirus Disease 2019 (COVID‐19) has led to a global pandemic in the year 2020 and the cases are dynamically increasing and active all over the world. COVID‐19 is caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2). It is a human‐to‐human transmissible disease which has severely affected people especially with weaker immunity, and is detected through Reverse Transcription Polymerase Chain Reaction (RT‐PCR). RT‐PCR is a lethargic process and therefore intelligent systems are proposed which uses chest images for early detection of COVID‐19. This paper proposes a regularized and attentive intelligent system called ‘Mixed Attention & Regularized COVID‐19 Network (MARCOV19‐Net)' for detection of COVID‐19 using chest X‐Ray images. The performance of MARCOV19‐Net is compared with VGG‐16, Regularized COVID‐19 Deep Convolutional Network (RCOV19‐DCNet) and Mixed Attention and unregularized COVID‐19 Network (MACOV19‐Net), and with other state‐of‐the‐art models. MARCOV19‐Net has achieved the highest F‐score, ROC and AUC of 98.76%, 99.4% and 99.6%, respectively. [ FROM AUTHOR] Copyright of International Journal of Imaging Systems & Technology is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Coronavirus Disease 2019 (COVID-19) is an evolving communicable disease caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which has led to a global pandemic since December 2019. The virus has its origin from bat and is suspected to have transmitted to humans through zoonotic links. The disease shows dynamic symptoms, nature and reaction to the human body thereby challenging the world of medicine. Moreover, it has tremendous resemblance to viral pneumonia or Community Acquired Pneumonia (CAP). Reverse Transcription Polymerase Chain Reaction (RT-PCR) is performed for detection of COVID-19. Nevertheless, RT-PCR is not completely reliable and sometimes unavailable. Therefore, scientists and researchers have suggested analysis and examination of Computing Tomography (CT) scans and Chest X-Ray (CXR) images to identify the features of COVID-19 in patients having clinical manifestation of the disease, using expert systems deploying learning algorithms such as Machine Learning (ML) and Deep Learning (DL). The paper identifies and reviews various chest image features using the aforementioned imaging modalities for reliable and faster detection of COVID-19 than laboratory processes. The paper also reviews and compares the different aspects of ML and DL using chest images, for detection of COVID-19.