CoronaNet: A Novel Deep Learning Model for COVID-19 Detection in CT Scans

Abstract

Coronavirus disease (COVID-19) is currently the cause of a global pandemic that is affecting millions of people around the world. Inadequate testing resources have resulted in several people going undiagnosed and consequently untreated; however, using computerized tomography (CT) scans for diagnosis is an alternative to bypass this limitation. Unfortunately, CT scan analysis is time-consuming and labor intensive and rendering is generally infeasible in most diagnosis situations. In order to alleviate this problem, previous studies have utilized multiple deep learning techniques to analyze biomedical images such as CT scans. Specifically, convolutional neural networks (CNNs) have been shown to provide medical diagnosis with a high degree of accuracy. A common issue in the training of CNNs for biomedical applications is the requirement of large datasets. In this paper, we propose the use of affine transformations to artificially magnify the size of our dataset. Additionally, we propose the use of the Laplace filter to increase feature detection in CT scan analysis. We then feed the preprocessed images to a novel deep CNN structure: CoronaNet. We find that the use of the Laplace filter significantly increases the performance of CoronaNet across all metrics. Additionally, we find that affine transformations successfully magnify the dataset without resulting in high degrees of overfitting. Specifically, we achieved an accuracy of 92% and an F1 of 0.8735. Our novel research describes the potential of the Laplace filter to significantly increase deep CNN performance in biomedical applications such as COVID-19 diagnosis.