aspg-22-3326-1791483610 20261008182010 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of BIM and Engineering Science IJBES 2571-1075 2024 9 2 N. Gobi Assistant Professor, School of Computer Science and IT, Jain (Deemed-to-be University), Bangalore, India M. Balakrishnan Professor, Karpagam College of Engineering, Coimbatore, India S. R. Indurekaa Assistant Professor, Dr.Mahalingam College of Engineering and Technology, Pollachi, India A. B. Arockia Christopher Professor & Head, Department of Master of Computer Applications, Rathinam Technical Campus, Coimbatore, India Automated feature extraction and segmentation of medical images are essential for accurate diagnostics, enabling the identification of relevant structures with minimal human intervention. This study introduces an Explainable AI (XAI) framework for automated feature extraction in medical image segmentation, aiming to enhance transparency in deep learning models used in medical imaging. The proposed framework uses a Convolutional Neural Network (CNN) with integrated attention mechanisms and layer-wise relevance propagation (LRP) to identify critical features while segmenting regions of interest. Testing on datasets of MRI brain scans and CT liver scans, the model achieved an accuracy of 94%, a Dice similarity coefficient (DSC) of 0.88, and an Intersection over Union (IoU) score of 0.83. These results outperform conventional CNN-based segmentation techniques by 10% on average, highlighting the framework's precision in identifying and segmenting intricate structures, including lesions and abnormalities. Additionally, the XAI components provide visual explanations of the segmentation process, enabling clinicians to understand which features influenced the model's decisions. This enhanced transparency is crucial for building trust in AI-driven medical solutions, ultimately facilitating their integration into clinical workflows. 2024 08 14 3326 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJBES.090202 https://www.americaspg.com/journal/22/article/3326 https://www.americaspg.com/storage/articles/manuscripts/01KZ9SGMQ9J7DTFVX6JPD1TH3G.pdf