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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) |
Files in This Item:
File | Description | Size | Format | |
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PasvantisKonstantinosMsc2024.pdf | 4.96 MB | Adobe PDF | View/Open |
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