A novel approach towards the classification of Bone Fracture from Musculoskeletal Radiography images using Attention Based Transfer Learning

Computer-aided diagnosis (CAD) is today considered a vital tool in the field of biological image categorization, segmentation, and other related tasks. The current breakthrough in computer vision algorithms and deep learning approaches has substantially enhanced the effectiveness and precision of apps built to recognize and locate regions of interest inside medical photographs. Among the different disciplines of medical image analysis, bone fracture detection, and classification have exhibited exceptional potential. Although numerous imaging modalities are applied in medical diagnostics, X-rays are particularly significant in this sector due to their broad availability, ease of use, and extensive information extraction capabilities. This research studies bone fracture categorization using the FracAtlas dataset, which comprises 4,083 musculoskeletal radiography pictures. Given the transformational development in transfer learning, particularly its efficacy in medical image processing, we deploy an attention-based transfer learning model to detect bone fractures in X-ray scans. Though the popular InceptionV3 and DenseNet121 deep learning models have been widely used, they still have the potential to be employed in crucial jobs. In this research, alongside transfer learning, a separate attention mechanism is also applied to boost the capabilities of transfer learning techniques. Through rigorous optimization, our model achieves a state-of-the-art accuracy of more than 90% in fracture classification. This work contributes to the expanding corpus of research focused on the application of transfer learning to medical imaging, notably in the context of X-ray processing, and emphasizes the promise for additional exploration in this domain.