Diagnosing Covid-19 using AI based Medical Image Analysis

ABSTRACT

The pandemic of COVID-19 is currently one of the most significant problems being dealt with, all around the world. It mainly affects the lungs of the infected person which can further result in serious threats. So to avoid this life threatening condition, we have used chest radiological images for COVID-19 detection. This infectious disease is communicable and is spreading rapidly throughout the world. Hence, fast and accurate detection of COVID-19 is mandatory, so one can be given proper treatment well before time. In this paper, the proposed work aims to develop a web application, namely CovSADs(Covid-19 Smart A.I. Diagnosis System), using deep learning approach for faster and efficient detection of COVID-19. This web application uses X-ray and CT scan images for the evaluation. Here, we have developed DeepCovX and DeepCovCT models by incorporating Transfer Learning (TL) approach for COVID-19 detection via chest X-ray and CT scan images respectively. Further, we have used GradCam in case of X-ray to make sure our model is looking at relevant information to make decisions and image-segmentation is used in case of CT scan to extract and localize Region-of-interest (ROI) from binary image. Our proposed models show the accuracy of 95.89% and 98.01% for X-ray and CT scan images respectively. We have obtained specificity of 99.57%, sensitivity of 100%, and AUC of 0.998 in case of X-ray and specificity of 98.80%, sensitivity of 97.06%, and AUC of 0.9875 in case of CT scan images. F1-score is obtained as 0.98 for COVID-19 and 0.98 for Non-COVID-19 in case of CT scan images. Both quantitative and qualitative results demonstrate promising results for COVID-19 detection and extraction of infected lung regions. The primary objective of the web application is to assist the radiologists not only for mass screening but also to help in planning treatment process. © 2022 ACM.