Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches
<|reference_start|>Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches: 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.<|reference_end|>
@article{pasvantis2024enhancing,
    title={Enhancing Deep Learning Model Explainability in Brain Tumor Datasets
  using Post-Heuristic Approaches},
    author={Konstantinos Pasvantis, Eftychios Protopapadakis},
    journal={arXiv preprint arXiv:2404.19568},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.19568},
    primaryClass={eess.IV cs.CV}
}