2571-1075ISSN (Online)
Volume 9 , Issue 2 , PP: 10-18, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
N. Gobi 1 * , M. Balakrishnan 2 , S. R. Indurekaa 3 , A. B. Arockia Christopher 4
Doi: https://doi.org/10.54216/IJBES.090202
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.
Explainable AI (XAI) , Medical Image Segmentation , Automated Feature Extraction , Convolutional Neural Networks (CNN) , Attention Mechanisms , Layer-wise Relevance Propagation (LRP) , Dice Similarity Coefficient (DSC) , Intersection over Union (IoU) , Model Transparency , Clinical Decision Support
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