ResCovNet: A Deep Learning-Based Architecture For COVID-19 Detection From Chest CT Scan Images

Automatic disease detection using machine learning-based techniques from X-ray and computed tomography (CT) can play a major role in the frontline to assist medical professionals during the current outbreak of COVID-19. Fast diagnosis of the disease is the key to reduce the uncontrollable spread of this life-threatening disease, where machine learning-based applications can contribute greatly by predicting the situation of patients so that professionals can decide accordingly. The major drawbacks of detecting COVID-19 are its similarities with different types of pneumonia, and the absence of properly labeled data. Considering the ResNet152V2 as a backbone network, an efficient architecture, namely ResCovNet is proposed to detect COVID-19 accurately from chest CT scan images by separating it from three types of pneumonia and normal cases. Otsu's thresholding is applied in the pre-processing step to strengthen the features for the classification network. With the use of proposed architecture, a very satisfactory classification accuracy of 88.1% is achieved to separate COVID-19 from all other four classes. Evaluating the performance of this study by 3-fold cross-validation, and comparison with related works prove that this adroit algorithm provides an effective way to be implemented as a diagnostic tool in the COVID-19 screening.