Please use this identifier to cite or link to this item: http://dspace.lib.uom.gr/handle/2159/30853
Author: Πασβάντης, Κωνσταντίνος
Title: Enhancing deep learning model explainability in brain tumor datasets using post-heuristic approaches
Date Issued: 2024
Department: Πρόγραμμα Μεταπτυχιακών Σπουδών στην Τεχνητή Νοημοσύνη και Αναλυτική Δεδομένων
Supervisor: Πρωτοπαπαδάκης, Ευτύχιος
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 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.
Keywords: Computer Vision
Health Informatics
Information: Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2024.
Rights: Αναφορά Δημιουργού - Παρόμοια Διανομή 4.0 Διεθνές
Appears in Collections:ΠΜΣ στην Τεχνητή Νοημοσύνη και Αναλυτική Δεδομένων (Μ)

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