Mixed attention and regularized COVID‐19 network: An approach to detection of COVID‐19 with chest x‐ray images

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.)