Efficient Feature Extraction Using MobileNetV2 and EfficientNetB0 for Multi-Class Brain Tumor Classification - Amelia, Hemas Anggita; Rahardi, Majid (Journal of Applied Informatics and Computing, 2025)
Brain tumor classification in MRI is complicated by the similarity of imaging features across multiple tumor classes.  This study evaluates the use of lightweight convolutional neural network (CNN) architectures as feature extractors combined with machine learning classifiers for multi-class classification. MobileNetV2 and EfficientNetB0 were used to extract fixed-length feature representations, which were then classified using Support Vector Machine (SVM), Logistic Regression, Random Forest, and K-Nearest Neighbors. The evaluation used stratified five-fold cross-validation, and performance was measured with accuracy, F1-score, and Matthews Correlation Coefficient (MCC). Results show that EfficientNetB0 features paired with SVM achieved the highest test accuracy (98.5%), while Logistic Regression also yielded competitive performance (97.1%). Class-wise analysis indicated strong results for pituitary and non-tumor cases. This work shows that lightweight CNN-based feature extraction may serve as a practical direction for improving multi-class brain tumor MRI classification, with potential benefits for applications in resource-limited environments.