Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2024.
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Approved for entry into archive by Κυριακή Μπαλτά (
[email protected]) on 2024-07-04T09:42:54Z (GMT) No. of bitstreams: 2
license_rdf: 1025 bytes, checksum: 84a900c9dd4b2a10095a94649e1ce116 (MD5)
Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches.pdf: 5070589 bytes, checksum: 5e52cedd80cf0b21dcd26c991e53a005 (MD5)
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Made available in DSpace on 2024-07-04T09:42:54Z (GMT). No. of bitstreams: 2
license_rdf: 1025 bytes, checksum: 84a900c9dd4b2a10095a94649e1ce116 (MD5)
Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches.pdf: 5070589 bytes, checksum: 5e52cedd80cf0b21dcd26c991e53a005 (MD5)
Previous issue date: 2024-07-04
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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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Submitted by ΚΩΝΣΤΑΝΤΙΝΟΣ ΠΑΣΒΑΝΤΗΣ (
[email protected]) on 2024-07-04T06:55:07Z
No. of bitstreams: 2
license_rdf: 1025 bytes, checksum: 84a900c9dd4b2a10095a94649e1ce116 (MD5)
Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches.pdf: 5070589 bytes, checksum: 5e52cedd80cf0b21dcd26c991e53a005 (MD5)
(EN)
University of Macedonia
