Robust Framework for Diagnosing Novel Corona Virus from CT images using Support Vector Binary Classifier

Abstract

The novel coronavirus (commonly abbreviated as CoVID-19) has emerged as a threat to the entire global civilization and has emerged as one of the most infectious and, at times, deadly viruses. Prompt discovery of this infection can assist medical supervisors in taking preventive actions to control the spread. Usually, radiologists and medical specialists require an average time of ~31 minutes to test the CT images and confirm the infection. A large dataset of more than 1000 patients has been gathered and randomly chosen for this experiment. In this research, a ready-to-deploy computer-aided diagnosis (CADx) to detect COVID-19 infection is introduced. A variety of deep learning architectures have been experimented to discover the most reliable predictive model for the diagnosis. This research uses the Densely Connected Convolution Network (DenseNet-121 architecture) along with a boosting support vector binary classifier to tell the difference between someone who has the coronavirus and someone who is healthy. The combination put forward in this work achieved 93% ± 1.8% accuracy, 94.9% ± 2.6% recall, 98% ± 1.5% precision, and an F1 score of 94% ± 1.7%. The model takes less than 1 second to process one image. On the grounds of the above findings, it can be concluded that the proposed approach can be used to diagnose novel coronavirus infections.