Classification of X-ray images using deep learningNELA, ARDIT (Avtor) Skočaj, Danijel (Mentor) neural networksdeep learningcomputer visionimage classificationmedical imagingAs technology continues to advance, many different fields are exploring the benefits of machine learning, and medicine is no exception. The application of machine learning in medicine has the potential to revolutionize the way we approach patient care and disease diagnosis. One area in which machine learning has shown particular promise is the analysis of medical imaging. For example, chest X-rays are a common diagnostic tool used to detect a variety of lung diseases, including pneumonia, hernia, and cardiomegaly. However, accurately interpreting X-ray images requires significant expertise and experience on the part of a medical professional. In the thesis, we discuss the problem of automatic classification of X-ray images. We use data collected and labeled by the NIH to train a state-of-the-art machine learning model based on deep neural networks to detect any issues in the X-ray scan. We compared different architectures and determined that ResNet101 and Transformers performed the best. Using these models alongside transfer learning resulted in mean AUC scores of approximately 0.795.20232023-12-04 15:28:01Diplomsko delo/naloga152708sl