Automated COVID 19 Detection from Chest X Ray Images A High Resolution Network (HRNet) Approach
The pandemic, originated by novel coronavirus 2019 (COVID 19) continuing its devastating effect on the health, well being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID 19 in the human body, to constraint the exposure and control the spread of the virus. Chest X Rays are one of the noninvasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time consuming. In this work, we propose an automated COVID 19 classifier, utilizing available COVID and non COVID X Ray datasets, along with High Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed dataset, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X Ray images into the diagnosis of the disease.