Explainability of Deep Neural Networks for Brain Tumor Detection

TL;DR

This study evaluates the explainability and performance of CNN and Transformer models for brain tumor detection, highlighting CNNs' superiority on small medical datasets and the role of XAI techniques in model interpretation.

Contribution

It compares CNN and Transformer models using XAI methods on real medical data, revealing CNNs' advantages for small datasets and providing insights into model interpretability.

Findings

CNNs outperform Transformers on small datasets

XAI techniques improve understanding of model decisions

Hyperparameter tuning enhances model performance

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.