Knee Osteoarthritis Classification Using X-Ray Images Based on Optimal Deep Neural Network

X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost. However, the manual categorization of knee joint disorders is time-consuming, requires an expert person, and is costly. This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm. Two pre-trained deep learning models (Efficientnet-b0 and Densenet201) have been employed for the training and feature extraction. Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images. In the next step, fusion is performed using a canonical correlation approach and obtained a feature vector that has more information than the original feature vector. After that, an improved whale optimization algorithm is developed for dimensionality reduction. The selected features are finally passed to the machine learning algorithms such as Fine-Tuned support vector machine (SVM) and neural networks for classification purposes. The experiments of the proposed framework have been conducted on the publicly available dataset and obtained the maximum accuracy of 90.1%. Also, the system is explained using Explainable Artificial Intelligence (XAI) technique called occlusion, and results are compared with recent research. Based on the results compared with recent techniques, it is shown that the proposed method’s accuracy significantly improved.