Razložljiva umetna inteligenca za diagnozo bolezni na osnovi slik

Accurate and timely diagnosis of diseases from medical images is a critical challenge in healthcare, often limited by the availability of expert clinicians and the complexity of image interpretation. This thesis addresses the problem of automated human disease diagnosis from medical images by developing and evaluating state-of-the-art deep learning models, as well as integrating explainable artificial intelligence (XAI) techniques. We systematically benchmarked a range of advanced architectures across multiple datasets and disease domains, including skin cancer, diabetic retinopathy, and pneumonia. Our approach emphasizes not only high diagnostic accuracy but also model transparency, with a particular focus on the quantitative evaluation of XAI methods such as CAM-based visualizations and example-based retrieval. The primary focus of this work and best results were achieved on the ISIC 2018 skin lesion dataset, where our methods reached a balanced multiclass accuracy of 85.1%, placing us among the top performers on the official leaderboard.