Enhancing Bone Abnormality Classification Through Background and Irrelevant Part Processing: A Deep Learning and Explainable AI Approach

Medical image classification has beenan important application for Deep Learning techniques for over a decade, and since the emergence of Explainable AI (XAI), researchers have started using XAI to validate the results produced by these black box models. In the research field, it has become clear that accuracy and efficiency are not the only crucial factors for developing medical deep learning models; the authenticity of results and the accountability of the model and its creator also matter greatly. The novelty of this paper lies in its specific application of extended preprocessing techniques-namely, the removal of background and irrelevant parts-to medical images for improving the performance of deep learning models in classification tasks. While the concept of preprocessing images has been explored by many researchers, applying such targeted preprocessing steps to medical images, combined with the use of XAI to validate and illustrate the benefits, is a novel approach. This paper highlights the unique requirements of medical image data and proposes an innovative method to enhance model accuracy and reliability in medical diagnostics by removing background and redundant features from the images.