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

Konstantinos Pasvantis; Eftychios Protopapadakis

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arXiv · 2404.19568

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

Konstantinos Pasvantis·Eftychios Protopapadakis

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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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Konstantinos Pasvantis, Eftychios Protopapadakis. 2024-04-30. Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches. https://arxiv.org/abs/2404.19568

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