Evaluating and Optimizing CNN–Transformer Architectures for Musculoskeletal Disease Classification
Year: 2025
Type: article
Abstract: This study examines the impact of dataset dimensionality on deep learning performance in musculoskeletal disease detection, focusing on osteoporosis and rheumatoid arthritis. Using over 200,000 annotated X-ray, DXA, and MRI images, the performance of Vision Transformer (ViT), ConvNeXt, and Swin Transformer models was systematically evaluated in terms of scalability, robustness, and multi-modal integration. Results demonstrate that increasing dataset scale significantly enhances model generalization, with Swin Transformer achieving the best performance (AUC = 0.94, p < 0.001). These findings underscore the critical role of self-attention mechanisms and model scaling strategies in medical image classification, providing new benchmarks for dataset requirements and guiding the development of more reliable AI-driven diagnostic systems. Furthermore, the study emphasizes the necessity of large, diverse datasets to mitigate overfitting and improve real-world applicability. It also highlights the potential of hybrid architectures for integrating multi-source medical data. Overall, this research contributes to advancing explainable and scalable AI solutions for musculoskeletal imaging in clinical practice.
Authors: Moulay Youssef Ichahane, Noureddine Assad
Institution: Chouaib Doukkali University
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7092370280
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