Computer Science > Computer Vision and Pattern Recognition
arXiv:2410.07613 (cs)
[Submitted on 10 Oct 2024]
Title:Explainability of Deep Neural Networks for Brain Tumor Detection
View a PDF of the paper titled Explainability of Deep Neural Networks for Brain Tumor Detection, by S.Park and J.Kim
View PDFHTML (experimental)Abstract:Medical image classification is crucial for supporting healthcare professionals in decision-making and training. While Convolutional Neural Networks (CNNs) have traditionally dominated this field, Transformer-based models are gaining attention. In this study, we apply explainable AI (XAI) techniques to assess the performance of various models on real-world medical data and identify areas for improvement. We compare CNN models such as VGG-16, ResNet-50, and EfficientNetV2L with a Transformer model: ViT-Base-16. Our results show that data augmentation has little impact, but hyperparameter tuning and advanced modeling improve performance. CNNs, particularly VGG-16 and ResNet-50, outperform ViT-Base-16 and EfficientNetV2L, likely due to underfitting from limited data. XAI methods like LIME and SHAP further reveal that better-performing models visualize tumors more effectively. These findings suggest that CNNs with shallower architectures are more effective for small datasets and can support medical decision-making.
| Comments: | 10 pages, 13 figures |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2410.07613 [cs.CV] |
| (orarXiv:2410.07613v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2410.07613 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Explainability of Deep Neural Networks for Brain Tumor Detection, by S.Park and J.Kim
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