Explainability of Deep Neural Networks for Brain Tumor Detection

• URL: http://arxiv.org/abs/2410.07613v1
• Date: Thu, 10 Oct 2024 05:01:21 GMT
• Title: Explainability of Deep Neural Networks for Brain Tumor Detection
• Authors: S. Park, J. Kim,
• Abstract summary: We apply explainable AI (XAI) techniques to assess the performance of various models on real-world medical data. CNNs with shallower architectures are more effective for small datasets and can support medical decision-making.
• Score: 0.0828720658988688
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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