Πρωτοπαπαδάκης, ΕυτύχιοςΠασβάντης, ΚωνσταντίνοςΔιπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2024.Approved for entry into archive by Κυριακή Μπαλτά (balta@uom.gr) 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)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: 2024-07-04The 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.Submitted by ΚΩΝΣΤΑΝΤΙΝΟΣ ΠΑΣΒΑΝΤΗΣ (aid23005@uom.edu.gr) 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)http://dspace.lib.uom.gr/handle/2159/30853Πανεπιστήμιο ΜακεδονίαςΑναφορά Δημιουργού - Παρόμοια Διανομή 4.0 Διεθνέςhttp://creativecommons.org/licenses/by-sa/4.0/Health InformaticsComputer VisionEnhancing deep learning model explainability in brain tumor datasets using post-heuristic approachesElectronic Thesis or DissertationText2024Τεχνητή νοημοσύνηArtificial Intelligenceisi - Computer Science, Artificial Intelligence covers resources that focus on research and techniques to create machines that attempt to efficiently reason, problem-solve, use knowledge representation, and perform analysis of contradictory or ambiguous information. This category includes resources on artificial intelligence technologies such as expert systems, fuzzy systems, natural language processing, speech recognition, pattern recognition, computer vision, decision-support systems, knowledge bases, and neural networks.Μεταπτυχιακή εργασίαMaster thesisΠανεπιστήμιο ΜακεδονίαςGreek Aggregator OpenArchives.gr | National Documentation Centre (EKT)