Multinomial Classification Deep Learning Approach for Knee Osteoporosis

Early osteoporosis detection is considered crucial for minimizing health consequences. The primary method for early osteoporosis detection is through bone density testing. However, this is time-consuming and not affordable for everyone. As a result, machine learning techniques have been introduced to improve the cost and performance of this early detection process. Unlike previous research, which deals only with osteoporosis detection as a binary classification problem, this research deals with the case as a multinomial classification problem by considering osteopenia as a separate class. This paper studies deep learning-based classification of knee osteoporosis utilizing transfer learning by fine-tuning the layers. The fine-tuning is carried out except for the normalization layers of the following networks: MobileNetV3-Large, InceptionRestNetV2, VGG19, and DenseNet121. These models are trained on an augmented version of the 1947 knee X-ray dataset. To further contribute to the existing research, the proposed method divides knee X-ray pictures into three classes: osteopenia, osteoporosis, and normal. This multinomial classification perspective makes it easier to initiate early screening and treatment. The collected results compare the computational efficiency of MobileNetV3-Large and the powerful feature extraction capabilities of InceptionResNetV2 and VGG19 by measuring model latency and classification performance. VGG19 demonstrated the best classification accuracy of 93.08%, followed closely by InceptionResNetV2 at 91.79%, DenseNet121 at 90.77% and MobileNettV3-Large at 90.43%. MobileNetV3-Large is better in execution time compared to these other models. A comparison with existing models highlights how the proposed fine-tuned models could offer a fast, scalable, and accurate solution for automated osteoporosis screening, supporting early detection and improved patient outcomes.