Abstract
Accurately classifying medical images with multiple labels is essential for early disease detection and enhancing clinical decision-making. In contrast to singlelabel classification, multi- label approaches allow for the simultaneous identification of multiple co- existing pathologies in a single image. Deep learning approaches, including convolutional neural networks and transformer- based models, have shown promising results, but they often suffer from high computational costs and lack of explainability, making them impractical for many medical applications. To address these challenges, this study introduces a novel lightweight transformer- based neural network optimized for multi- label medical image classification, reducing computational complexity while preserving strong feature extraction capabilities. Evaluations on the ChestX- ray11 dataset show superior classification accuracy and computational efficiency compared to existing methods. Furthermore, Grad- CAM++ visualizations enhance interpretability by highlighting disease- relevant regions, fostering trust in medical AI applications.
