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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Seminar Nasional Teknologi Informasi Komunikasi dan Industri Indonesian Journal of Applied Technology and Innovation Science
Mustakim Mustakim
Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

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Application of Deep Learning for the Classification of Brain Tumor Magnetic Resonance Imaging Images Rizki Andreas; Mustakim Mustakim; Ma. Angelica M. Qin; Safril Siregar
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 3 No. 2 (2026): IJATIS August 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i2.3089

Abstract

Accurate and timely classification of brain tumors from Magnetic Resonance Imaging (MRI) is essential for supporting clinical diagnosis and treatment planning. This study presents a comparative evaluation of transfer learning-based Convolutional Neural Network (CNN) architectures, including ResNet-50, DenseNet121, and EfficientNet-B2, for binary brain tumor MRI classification (Glioma and Meningioma). The dataset was augmented to improve model generalization, and the data were split into training and test sets using an 80:20 hold-out split. The models were trained using Adam and RMSProp optimizers with different learning rates, and their performance was evaluated using accuracy, precision, recall, and F1-score. Experimental results demonstrate that data augmentation significantly enhances classification performance across all evaluated architectures. Among the tested models, ResNet-50 with the RMSProp optimizer and a learning rate of 0.001 achieved the best performance, yielding the lowest training and validation losses and perfect classification results on the test set, with accuracy, precision, recall, and F1-score approaching 100%. DenseNet121 and EfficientNet-B2 also achieved excellent performance but were slightly inferior to ResNet-50. These findings indicate that transfer learning with ResNet-50 and RMSProp provides an effective and reliable solution for automated brain tumor MRI image classification
Co-Authors Ahmad Jaini Astia Weni Syaputri Dessi Cahyanti Devy Safira Ega Dwi Lestari Ega Kuslia Sari Ega Kuslia Sari Insanul Kamila Jeni Adhiva Ma. Angelica M. Qin Nurfadila Utami Rahmatul Annisa Rizki Andreas Safril Siregar Said Thaufik Rizaldi Said Thaufiq Rizaldi Sella Annisa Shasa Elfina Rahmadani Shinta Ayunda Putri Sindhy Genjang Setyorini Siti Syahidatul Helma Taufiq Qurahman