Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches
This work addresses the problem of opaque decision-making in AI for medical professionals, though it appears incremental as it builds on existing LIME methods.
The study tackled the lack of explainability in deep learning models for medical diagnosis by enhancing the interpretability of LIME-based explanations through post-processing rules, resulting in more robust and concrete outcomes for brain tumor detection.
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.