Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches

• URL: http://arxiv.org/abs/2404.19568v1
• Date: Tue, 30 Apr 2024 13:59:13 GMT
• Title: Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches
• Authors: Konstantinos Pasvantis, Eftychios Protopapadakis,
• Abstract summary: This study addresses the inherent lack of explainability during decision-making processes. The primary focus is directed towards refining the explanations generated by the LIME Library and LIME image explainer. Our proposed post-heuristic approach demonstrates significant advancements, yielding more robust and concrete results.
• Score: 1.325953054381901
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The application of deep learning models in medical diagnosis has showcased considerable efficacy in recent years. Nevertheless, a notable limitation involves the inherent lack of explainability during decision-making processes. This study addresses such a constraint, by enhancing the interpretability robustness. The primary focus is directed towards refining the explanations generated by the LIME Library and LIME image explainer. This is achieved throuhg post-processing mechanisms, based on scenario-specific rules. Multiple experiments have been conducted using publicly accessible datasets related to brain tumor detection. Our proposed post-heuristic approach demonstrates significant advancements, yielding more robust and concrete results, in the context of medical diagnosis.

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