Leveraging RegNetX002 for Automated Classification of COVID-19 and Other Lung Opacities in Chest X-ray Images

Abstract

The global corona virus epidemic has affected medical treatment. Because there are so many variations of newly developing infectious viruses, both the dangers they pose, and the effectiveness of vaccinations need much attention. To better comprehend this illness and investigate its transmission, individual diagnosis, and maybe other fascinating related concerns, it is helpful to learn more complex and interpretable models using COVID-19 data. On the other hand, the lack of accurately classified data is creating certain complications and challenges. Utilizing pre-trained Deep Neural Network (DNN) models on big datasets such as ImageNet has been done in earlier publications. This study evaluated the X002 version of the RegNet CNN architecture. Due to its less computationally costly design and very small number of parameters, the RegNet model is more computationally feasible for imaging applications compared to other CNN models. Self-regulation is a regulatory module that RegNet employs; it retrieves spatio-temporal data from the network's intermediate levels. Furthermore, RegNet's weight residual connections, batch normalization, and regularization mechanism approaches make it fast, scalable, and versatile. Our suggested approach successfully classified COVID-19, normal, pneumonia, and other lung opacities with an astounding accuracy of 95.37%, as determined after thorough testing and review.