Transfer Learning with Fine-Tuning on MobileNet and GRAD-CAM for Bones Abnormalities Diagnosis

Osteoarthritis is a common medical condition. Unfortunately, despite the support of X-ray imaging technology in diagnosis, the accuracy of diagnostic results still depends on human factors. Furthermore, when errors do occur, they are often detected late, leading to a waste of time, money, and even disability for the patient. This study has deployed and evaluated transfer learning techniques in abnormal and normal bone images classification on X-ray images collected from the dataset of MUsculoskeletal RAdiographs (MURA) with 17,367 images and then leveraged techniques for results explanations of learning algorithms such as Gradient-weighted Class Activation Mapping (GRAD-CAM) to provide visual highlighted interesting areas in the images which can be signals for anomalies in bones. The classification performance using MobileNet with techniques of hyper-parameters fine-tuning can reach an accuracy of 0.84 in abnormal and normal bone classification tasks on the wrist, humerus, and elbow.