Explainability of Deep Neural Networks for Brain Tumor Detection
<|reference_start|>Explainability of Deep Neural Networks for Brain Tumor Detection: 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.<|reference_end|>
@article{park2024explainability,
    title={Explainability of Deep Neural Networks for Brain Tumor Detection},
    author={S.Park and J.Kim},
    journal={arXiv preprint arXiv:2410.07613},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07613},
    primaryClass={cs.CV}
}