Knee Osteoarthritis Classification Using EfficientNet B3 Transfer Learning Model

Knee osteoarthritis (OA) is a chronic degenerative joint ailment characterized by pain, stiffness, edoema, and a limited range of motion in the knee joint. Boneon-bone contact occurs when the cartilage in the knee joint wears away. Deep neural networks are capable of performing a wide range of machine learning tasks in medical industries, such as diagnosis, prediction, and picture classification. This study categorizes knee osteoarthritis into five levels: minimal, healthy, moderate, suspect, and severe. For the categorizing task. EfficientNet B3 was used to train the model for 10 epochs with Adam as the optimizer and a batch size of 32. The model was trained on a fraction of the training data at each epoch to decrease the error between predicted classifications and actual labels. The model's accuracy was tested using a different validation dataset of 2,578 photos. The study's findings revealed that the model had an overall accuracy of 92%. The study's findings show that machine learning models have the potential to diagnose and treat knee osteoarthritis, while further research is needed to enhance the model's performance on bigger and more diverse datasets.