Spine Abnormality Detection Using GAN And Deformed CNN

Early diagnosis of spinal abnormalities is an important to prevent serious conditions of patients and facilitates earlier medical intervention. Nevertheless automatic classification of spinal irregularities from X-ray images is a complex task due to uneven class distributions, disparities in vertebral bodies, and a limited availability of annotative medical images. To solve these issues, this work investigates a deep learning framework that combines GANs for synthetic data generation and DCNs for strong feature extraction. Namely, the GAN model creates high-quality, class-balanced synthetic spinal X-ray images, which increases the diversity of less-represented abnormality categories. These improved datasets are then used to train a DCN-based multi-class classifier, in which deformable convolution layers adaptively sample the geometric features. This automatically improves the localization of irregular spinal structures. The experimental results have demonstrated that GAN and DCN considerably improve the performance of traditional CNN methods by a large margin, such as accuracy and sensitivity, and per-class AUC for various types of spinal abnormalities. A combination of generative data creation and flexible feature learning effectively reduces data scarcity and anatomical differences, thus providing promise for the application to support clinical decisions and the automation of spinal health assessment.