CoronaNeXt Evaluating the Performance of the Laplacian Operator in Diagnosing COVID-19 from Chest X-Rays

In recent years, usage of deep learning models for medical image classification tasks has grown exponentially due to their state of the art accuracy and efficiency; however, the performance of these models are often limited by insufficient publicly available data. In this study, we continue our previous work in exploring the applications of the Laplace Operator, a detail enhancing image filter, in deep learning models in order to overcome these performance plateaus. Specifically, we evaluate the performance of ResNet-18 in diagnosing COVID-19 from a relatively small dataset of X-ray images. When comparing the performance of our model, dubbed CoronaNeXt, on images without the Laplacian applied to images with the Laplacian applied, we find significant increases in all maximum validation metrics: accuracy improved from 87.6% to 94.8%; F1 score improved from 0.860 to 0.968; specificity improved from 0.865 to 0.944; and sensitivity improved from 0.885 to 0.992. Based on these results, we describe the potential of the Laplacian Operator in drastically improving the performance of deep learning architectures in medical image classification tasks, particularly when utilizing small to medium sized datasets. Notably, sensitivity underwent the most significant improvement, correlating with the results achieved in our previous work using the CT modality. We hope our research will spark further exploration of the Laplace Operator and other derivative-based image preprocessing methodologies in conjunction with powerful deep learning models for medical image tasks. Keywords— COVID-19, Chest X-rays, Laplace Operator, Deep Learning