Deep learning has shown impressive results in computer vision, but it often underperforms in imbalanced datasets. This challenge is prominent in medical imaging, particularly in the classification of musculoskeletal disorders, where minority classes are underrepresented. This study presents a generative framework for the detection of musculoskeletal abnormalities with a specific focus on the problem of data imbalance in the classification of radiographic images. A disentanglement-driven approach is employed using \(\beta \) -variational autoencoder ( \(\beta \) -VAE), which facilitates the generation of diverse and class-consistent samples through latent space manipulation. These synthetic samples are utilized within a triplet network for metric learning, enhancing discriminative representation by promoting greater inter-class separability and intra-class compactness in the latent space, thus effectively mitigating imbalance during classification. Experimental evaluation of the musculoskeletal radiograph (MURA) dataset, the proposed triplet network with \(\beta \) -VAE improves classification performance, achieving \(11.3\%\) higher accuracy and \(42.3\%\) greater Cohen’s kappa on the finger study type (DenseNet-169), and \(15.6\%\) accuracy gain with \(27.1\%\) Cohen’s kappa improvement on the forearm study type (ResNet-50), demonstrating its effectiveness for imbalance-aware diagnostic imaging.