Klasifikasi Tumor Otak pada Citra MRI Menggunakan Transfer Learning EfficientNetB1 dan Visualisasi Grad-CAM - Suyanti; Ophelia S, Chandy; Aryani, Lies; Saputra, Chindra; Prayitno (Jurnal Manajemen Teknologi Dan Sistem Informasi (JMS), 2026)
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.