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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Muhammad Abdul Ghofur
Department of Information Systems, Faculty of Science and Technology, Universitas Nahdlatul Ulama Sunan Giri
Computer Science & IT

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Brain Tumor Classification in MRI Images Using Convolutional Neural Networks with Explainable Artificial Intelligence Muhammad Abdul Ghofur; Nirma Ceisa Santi; Hastie Audytra
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13594

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.
Co-Authors Hastie Audytra Nirma Ceisa Santi