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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer
Abd Salam At Taqwa
State University of Makassar
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Lightweight Multi-Model CNN Fusion of ResNet50v2 and MobileNetv2 for Accurate Brain Tumor Classification on MRI Scans Abd Salam At Taqwa; Muhammad Fadhlullah; La Ode Fefli Yarlin
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 10 No. 1 (2026)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v10i1.1894

Abstract

Brain tumor classification remains a critical challenge in medical imaging because manual diagnosis from Magnetic Resonance Imaging is time-consuming and may produce inconsistent interpretations. Automated approaches using deep learning have shown promising results, although single-model methods may still face limitations in generalization and stability. This study introduces a lightweight multi model Convolutional Neural Network that combines MobileNetV2 and ResNet50V2 as dual-backbone feature extractors. Mo-bileNetV2 supports computational efficiency, while ResNet50V2 strengthens residual feature learning. The Bangladesh Brain MRI Dataset, which contains 6,056 images in three categories, Brain Glioma, Brain Menin-gioma, and Brain Tumor, was used in this study. All images were resized to 224 × 224 pixels before feature extraction, fusion, and classification. The proposed multi-model achieved 99.56% training accuracy and 93.37% validation accuracy, outperforming MobileNetV2 with 98.37% and 89.60 percent, and ResNet50V2 with 97.55% and 86.17 percent. On the test set, it reached 94.89% accuracy, 0.1536 loss, and 0.991 ROC AUC. These results show that integrating lightweight and deep architectures can improve robustness and accuracy while maintaining efficiency, making this approach suitable for real-world clinical support in brain tumor diagnosis.
Co-Authors La Ode Fefli Yarlin Muhammad Fadhlullah