Klasifikasi Tumor Otak pada Citra MRI Menggunakan Transfer Learning EfficientNetB1 dan Visualisasi Grad-CAM

Suyanti Suyanti; Chandy Ophelia S; Lies Aryani; Prayitno Prayitno

doi:10.61132/prosemnasproit.v2i2.112

Authors

Suyanti Suyanti Universitas Dinamika Bangsa
Chandy Ophelia S Universitas Dinamika Bangsa
Lies Aryani Universitas Dinamika Bangsa
Prayitno Prayitno Univesitas Dinamika Bangsa

DOI:

https://doi.org/10.61132/prosemnasproit.v2i2.112

Keywords:

brain tumor, MRI, EfficientNet, transfer learning, Grad-CAM, pipeline

Abstract

Magnetic resonance imaging (MRI) provides rich anatomical contrast for brain tumor assessment, yet routine interpretation remains time-intensive and demands high precision. This work develops a pipeline for four-class brain MRI image classification (glioma, meningioma, pituitary tumor, and no tumor) by combining automated brain-region cropping, data augmentation, and transfer learning with EfficientNetB1. Experimental results demonstrate exceptional performance, achieving an overall accuracy of 0.99 (99%) on the test set. Specifically, the model reached an F1-score of 1.00 for the no tumor class, 0.99 for pituitary, and 0.98 for both glioma and meningioma classes. Beyond reporting numerical performance, the study utilizes Grad-CAM heatmaps to verify that predictions rely on clinically plausible regions rather than spurious background cues. These results indicate that an efficiency-oriented backbone, paired with systematic preprocessing, can achieve reliable and interpretable performance for brain tumor classification tasks.

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