RESUMO
Advancements in technology have enabled diverse tools and medical devices that are able to improve the efficiency of diagnosis and detection of various health diseases. Rheumatoid arthritis is an autoimmune disease that affects multiple joints including the wrist, hands and feet. We used YOLOv5l6 to detect these joints in radiograph images. In this paper, we show that training YOLOv5l6 on joint images of healthy patients is able to achieve a high performance when used to evaluate joint images of patients with rheumatoid arthritis, even when there is a limited number of training samples. In addition to training joint images from healthy individuals with YOLOv5l6, we added several data augmentation steps to further improve the generalization of the deep learning model.
