A Hybrid Deep Learning Approach for Bone Defect Detection using DCGAN and CNN

Abstract

Bone fractures, caused by accidental traumas and medical disorders, can harm the human skeletal system, which provides structural support and protects vital organs. Leveraging Synthetic Image Generation via Generative Adversarial Networks (GANs) greatly improves bone defect accuracy in diagnosis by providing diverse and vast data sets for training. This novel approach results in stronger as well as precise diagnostic models, which enable early and reliable identification of diverse bone diseases. Here, CNNs are utilized in bone fracture images particularly in identifying trends and objects in complex visual inputs of bone fractures. The classification model performed admirably, with an overall accuracy of 98%, as well as high precision, recall, and F1-scores of around 98% across both categories, indicating its ability to distinguish among both categories with impressive reliability and consistency. The next phase of this research is to broaden GAN capabilities to address a broader range of medical imaging challenges, with the promise of not only enhancing diagnostic precision across multiple conditions but also personalizing methods of treatment, thus creating new standards in healthcare creativity and patient care.

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