LeNet-Xception: An Advanced Deep Learning Model for Early Covid-19 Detection from CT Scan Images

The COVID-19 pandemic has necessitated the deep learning, a subset of artificial intelligence, has had noteworthy development in the field of COVID-19 identification. Deep learning algorithms can analyse medical images, like CT scan images to aid in the swift and precise diagnosis of COVID-19. Deep learning models, such as LeNet and Xception, have been used in recent studies to diagnose COVID-19 from images of CT with high accuracy. This paper presents a deep learning approach for the detection of COVID-19 using computed tomography (CT) images by proposing a hybrid model, called LeNet-Xception. Various performance metrics, including specificity, sensitivity, and accuracy, were used to estimate the performance of the presented method. LeNet-Xception model attained an accuracy of 95.9%, a sensitivity of 97.5%, and a specificity of 93.8%. According to the results, the suggested technique suggests that can precisely identify cases of COVID-19 by utilising images of CT scans with high accuracy.