Nhlapho, W.; Atemkeng, M.; Brima, Y.; Ndogmo, J.-C. Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images. Information2024, 15, 182.
Nhlapho, W.; Atemkeng, M.; Brima, Y.; Ndogmo, J.-C. Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images. Information 2024, 15, 182.
Nhlapho, W.; Atemkeng, M.; Brima, Y.; Ndogmo, J.-C. Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images. Information2024, 15, 182.
Nhlapho, W.; Atemkeng, M.; Brima, Y.; Ndogmo, J.-C. Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images. Information 2024, 15, 182.
Abstract
The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumor from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making processes, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification.
Leveraging state-of-the-art DL frameworks with transfer learning, we conduct a comprehensive evaluation encompassing both classification accuracy and interpretability. Using state-of-the-art DL frameworks with transfer learning, we conduct a thorough evaluation covering both classification accuracy and interpretability. We employ adaptive path-based techniques to understand the underlying decision-making mechanisms of these models.
Grad-CAM and Grad-CAM++ highlight critical image regions where the tumors are located.
Keywords
transfer learning; deep learning; brain tumor classification; explanability and interpretability; Grad-Cam++; integrated gradients
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
