Automatic Diagnosis of Knee Osteoarthritis Using Deep Learning Approaches

Knee osteoarthritis is a common degenerative joint disease that affects millions of people worldwide, especially those aged over 50 years. It is characterized by gradual breakdown of the cartilage in the knee joint, leading to pain, stiffness, and reduced mobility. Knee osteoarthritis can develop because of a variety of factors, including genetics, old injuries, obesity, and repetitive stress on the knee joint. It is a chronic condition that often worsens over time if not treated properly. Various treatment options are available for managing knee osteoarthritis symptoms, including medication, physical therapy, and surgery. Early diagnosis and intervention can help individuals with knee osteoarthritis to manage their symptoms and maintain their quality of life. In this research, a range of deep learning models are utilized to aid clinicians in the diagnosis of knee osteoarthritis, reduce the workload on primary radiologists, and facilitate early detection and treatment. The deep learning model EfficientNetB7 implemented and compared with DenseNet169 and Inception ResNet V2. The EfficientNetB7 model achieved the highest results, with Accuracy 93.90%.