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

arXiv:2404.19568v1 Announce Type: cross
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 …

abstract application arxiv brain cs.cv datasets decision deep learning diagnosis eess.iv explainability focus interpretability making medical processes robustness study type