Comparison of CNN Performance in Brain Tumor Classification using an Explainable AI Approach

Abstract

Brain tumors are life-threatening conditions that require accurate and timely diagnosis. In clinical practice, diagnosis is commonly performed through Magnetic Resonance Imaging (MRI) analysis by medical experts, which is time-consuming and subject to variability. Recent advances in Convolutional Neural Networks (CNN) have shown promising performance in medical image classification; however, their black-box nature limits clinical trust and interpretability. This study proposes a comparative analysis of CNN-based models integrated with Explainable Artificial Intelligence (XAI) to improve both classification performance and model transparency. A transfer learning approach is employed using two architectures, ResNet50 and MobileNetV2, on a multi-class brain tumor MRI dataset. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to visualize discriminative regions influencing model predictions. Experimental results demonstrate that ResNet50 achieves an Accuracy of 93,7%, outperforming MobileNetV2 with 92.93%. Furthermore, Grad-CAM visualizations consistently highlight tumor-relevant regions, indicating that the models learn meaningful spatial features for classification. These findings confirm that integrating CNN with XAI not only provides high classification Accuracy but also improves model interpretability, thereby increasing its potential applicability in supporting reliable and transparent medical diagnosis.