Enhancing deep learning model explainability in brain tumor datasets using post-heuristic approaches
Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2024.
| Πρώτος συγγραφέας: | Πασβάντης, Κωνσταντίνος |
|---|---|
| Επόπτης Καθηγητής: | Πρωτοπαπαδάκης, Ευτύχιος |
| Μορφή: | Electronic Thesis or Dissertation |
| Γλώσσα: | English |
| Άλλες Λεπτομέρειες Έκδοσης: |
Πανεπιστήμιο Μακεδονίας,
2024
|
| Τμήμα: | Πρόγραμμα Μεταπτυχιακών Σπουδών στην Τεχνητή Νοημοσύνη και Αναλυτική Δεδομένων |
| Θέματα/Λέξεις Κλειδιά: | |
| Διαθέσιμο Online: |
http://dspace.lib.uom.gr/handle/2159/30853 |
| Περίληψη: |
Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2024. |
|---|---|
| Επιτομή: |
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. |