AI-Powered Bone Fracture Detection From X-Ray Images

Bone fracture detection is currently performed by manual radiologists via X-ray analysis, which is time-consuming and prone to errors. In order to evade such limitations, this study proposes adopting deep learning for the design of an automatic detection system for fractures. The suggested model utilizes the DenseNet201 architecture, which is widely known to support the representation and utilization of intricate features for classification. For gaining more exposure of fracture attributes, pre-processing techniques such as CLAHE and Wavelet Transform are employed nowadays. The system accurately classifies X-ray images into fractured and non-fractured classes with high precision, recall, AUC-ROC, and accuracy. The AI-based solution will help relieve radiologists from diagnosis workload and time while providing consistent, scalable, and real-time diagnosis, which is very useful in remote or low-resource healthcare environments. The proposed system illustrates the revolutionizing power of deep learning in medical diagnostics.