Publication

Abstract : We propose an advanced framework for automated fracture detection in medical X-ray images., harnessing the power of hybrid deep learning methodologies. Through the fusion of autoencoders” adeptness in feature extraction and a convolutional neural networks (CNN) efficiency in recognizing the complex patterns., the proposed model achieves remarkable accuracy in identifying fractures. By integrating data augmentation and preprocessing strategies., the system demonstrates enhanced robustness in handling variations in image quality and presentation. This innovation holds substantial promise in revolutionizing the field of medical diagnostics by offering a reliable and efficient tool for clinicians to expedite fracture diagnosis., facilitate treatment decisions., and ultimately improve patient care. The amalgamation of diverse techniques not only establishes a robust fracture detection mechanism but also showcases the potential of interdisciplinary approaches. The proposed approach demonstrated excellent accuracy in fracture detection with a 92% percentage., which can greatly improve medical diagnosis and treatment planning.