Study of Various Deep Learning Models for COVID-19 Detection Based on Fractional Order Optical Flow

The world entered into a new phase in the wake of Covid-19, which is declared as a global threat because countless people were losing their lives worldwide due to this disease every day. Even the best medical facilities were crumbling due to the rise of COVID-19 and lack of funding. However the use of nucleic acid-based testing generates an excessive amount of bio-waste and increases the risk of infection to the healthcare personnel. These tests take a lot of time, which contributes to the high mortality rate. To address these challenges, vision based techniques have been demonstrated to be beneficial in the early and swift diagnosis of COVID-19. Earlier imaging techniques used X-ray or CT-scan images to detect COVID-19. Consequently, these techniques do not filter out non-COVID cases, resulting in longer processing time. In this paper, COVID-19 identification is accomplished through the dynamic characteristics obtained from chest X-ray (CXR) images. These dynamic characteristics are derived as optical flow (OF). Moreover a fractional order-based variational model is used to estimate OF from two consecutive images of lungs. The novelty of this work is to use OF instead of images in COVID-19 detection. Further, the classification of lung dynamic features is performed with a convolutional neural network (CNN) in coordination of various machine learning (ML) classifiers. A detailed comparison is provided to show the significance of the algorithm.