Detection of single and dual pulmonary diseases using an optimized vision transformer

Abstract Pulmonary diseases rank among the leading causes of mortality worldwide, underscoring the importance of early detection to enhance patient outcomes and reduce fatalities. Chest X-rays (CXRs) serve as a critical diagnostic tool for identifying lung diseases; however, many pulmonary conditions exhibit similar patterns on CXRs, making differentiation challenging even for experienced radiologists. Furthermore, a single CXR may reveal multiple coexisting diseases, such as pneumonia and pleural effusion. Moreover, most existing studies in this field have been constrained to a limited number of classes and datasets, leaving significant gaps in the classification of diverse pulmonary conditions, particularly in remote regions where access to skilled radiologists is scarce. Recently, vision transformers (ViTs) have become a key technique in deep learning, and they have been widely used to detect different diseases from CXRs. This study aimed to adapt and optimize the pre-trained ViT-B16 model to predict and distinguish between single and dual pulmonary diseases whose diagnoses relied only on CXRs. The goal was to discriminate eight classes of evidence, which consisted of individual diseases (COVID-19, pneumonia, pneumothorax, tuberculosis, and pleural effusion), dual diseases (pneumonia and pleural effusion as well as pneumothorax and pleural effusion), and normal status. The experimental results show that the proposed model distinguished more classes than previous methods, with an accuracy of 98.18%. To the best of our knowledge, this is the first study to attempt to discriminate eight classes of evidence concerning pulmonary diseases, including both individual and dual diseases.