Improved Vision Transformer with Lion Optimizer for Lung Diseases Detection

Lung infections, such as pneumonia, bronchitis, tuberculosis, and notably COVID-19 caused by the SARS-CoV-2 virus, have caused widespread devastation globally, resulting in a significant loss of life. Timely and precise diagnosis of these respiratory diseases is crucial in controlling their spread and reducing their deadly impact. However, diagnostic errors can occur due to factors like physician workload and the need for a second opinion. To address these challenges, artificial intelligence-based diagnostic systems, utilizing deep learning algorithms, particularly in the radiology field, have been proposed. In this research, we introduced a novel model based on Multi-Axis Image Transformers, which boasts a reduced parameter count, decreased GPU computational load, real-time diagnostic capabilities, and improved accuracy. Furthermore, we conducted a detailed performance comparison of optimization algorithms, including SGD, Adam, and Lion, with higher results indicating that the Lion optimizer notably enhances the diagnostic capabilities of the proposed MaxViT model, especially in detecting lung infections. Our proposed approach underwent rigorous experimentation using the COVID-QU-Ex dataset, recognized as the most current, comprehensive, and balanced dataset for lung infections and COVID-19. Our method achieved diagnostic accuracy of 97.14%, surpassing existing models while maintaining significantly fewer parameters.