Comparative Analysis of Transfer Learning Approaches for Enhanced Osteoarthritis Detection and Diagnostic Accuracy

Osteoarthritis leads to disability among millions, and early diagnosis has greatly played a crucial role in its management. However, current diagnostic approaches are vague and unreliable with respect to their accuracy. The present study introduces a comparative analysis between three state-of-the-art deep transfer learning models with the use of InceptionV3, CNN, and Xception applied to the knee joint's X-rays in osteoarthritis detection. The pre-training process of the models for feature extraction helped to enhance the precision of diagnostic capability and the efficiency associated with the identification of OA. Each model was further refined and optimized for OA detection on a diverse dataset with data augmentation to boost robustness and generalizability. These models were then compared using metrics that were tracked over the test set, including accuracy, precision, recall, and F1 score to identify the most suitable one. Results show that though InceptionV3 yielded a balanced performance across all metrics, CNN led to better recall and improvement in detecting OA-positive cases. Inception demonstrated high precision as well, thereby minimizing the false positives, so it is an extremely effective device within the clinic when it makes a necessity to have high specificity. This comparative analysis describes the advantages of each architecture toward diagnosis of OA and their potential in deep transfer learning toward the help of experts in early and accurate detection of OA.