Enhanced Osteoarthritis Classification through Transfer Learning and Hyperparameter-Tuned Multi-Layer Ensemble Models

Knee osteoarthritis (OA) significantly limits activity and causes physical disability in older adults. Early classification of OA is crucial to slowing its progression. This paper introduces a method for OA classification using a transfer learning fusion network with hyperparameter tuning and a Multi-Layer Ensemble approach. The process begins by balancing the dataset through data augmentation and then integrates a pre-trained model with a customized CNN model. The architecture includes four convolutional blocks with varying filter sizes ($32,64,128$, and 256), dropout layers ($0.1,0.3,0.5$, and 0.7), and Mish activation functions. The flattened output of the last MaxPool2D layer is fed into three fully connected layers (256,128, and 5 neurons) with Softmax activation for multi-class prediction. The performance of various models is combined through a Multi-Layer Ensemble, resulting in superior performance with $\mathbf{7 5. 7 3 \%}$ accuracy. Our proposed method achieves a well-performing model for OA classification and overcomes prior limitations, emphasizing the importance of automated knee OA classification and providing an effective solution.