Lightweight early detection of knee osteoarthritis in athletes.

Osteoarthritis (OA) is a prevalent condition among athletes, characterized by the progressive degradation of joint cartilage, particularly in weight-bearing joints such as the knees. Early detection is critical for effective management and prevention of long-term complications. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown promise in medical diagnostics. In this study, we propose a novel approach for early-stage OA detection using an optimized EfficientNet-B0 architecture enhanced with the Efficient Channel Attention (ECA) module. This integration addresses the limitations of traditional attention mechanisms, such as Squeeze-and-Excitation (SE) blocks, by providing lightweight and computationally efficient feature recalibration. Our methodology is evaluated using the Knee Osteoarthritis Severity Grading Dataset, focusing on binary classification between healthy and early-stage OA cases. Comprehensive experiments demonstrate that the proposed model achieves superior accuracy, precision, and recall compared to baseline and State-of-the-Art (SOTA) architectures, including ResNet-50, VGG-16, and DenseNet, while maintaining minimal computational overhead. Class Activation Maps (CAMs) further validate the model capability to localize clinically relevant features, such as joint space narrowing and osteophyte formation, indicative of OA progression. This research not only sets a new benchmark for automated OA diagnostics but also emphasizes the importance of balancing high performance with resource efficiency. The proposed model lightweight architecture and robust diagnostic capabilities make it a strong candidate for real-time clinical applications, paving the way for improved patient outcomes through early intervention.