Brain Tumor Classification in MRI Images Using Convolutional Neural Networks with Explainable Artificial Intelligence

Muhammad Abdul Ghofur; Nirma Ceisa Santi; Hastie Audytra

doi:10.30871/jaic.v10i4.13594

Authors

Muhammad Abdul Ghofur Department of Information Systems, Faculty of Science and Technology, Universitas Nahdlatul Ulama Sunan Giri
Nirma Ceisa Santi Department of Information Systems, Faculty of Science and Technology, Universitas Nahdlatul Ulama Sunan Giri
Hastie Audytra Department of Information Systems, Faculty of Science and Technology, Universitas Nahdlatul Ulama Sunan Giri

DOI:

https://doi.org/10.30871/jaic.v10i4.13594

Keywords:

EfficientNetB0, ResNet50, Transfer Learning, 5-Fold Cross-Validation

Abstract

Brain tumors are a condition requiring rapid and accurate diagnosis. This study aims to enhance the transparency of Convolutional Neural Network (CNN)-based brain tumor classification models by implementing Explainable Artificial Intelligence (XAI) techniques, specifically Eigen-CAM and LIME. The models were developed using a transfer learning approach on ResNet50 and EfficientNetB0 architectures, utilizing a dataset of 3,000 MRI images categorized into glioma, meningioma, and pituitary tumor classes. Test results indicate that ResNet50 achieved the best performance, with accuracy, precision, recall, and F1-score values of 94%, while EfficientNetB0 achieved 93%. The application of 5-fold cross-validation improved the models' generalization capabilities and reduced the risk of overfitting. Visualizations using Eigen-CAM and LIME demonstrate that the models focus on relevant tumor regions, thereby increasing the transparency and reliability of MRI-based classification.

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