MobileNetV2 with transfer learning for brain tumor classification - Aziz Srai (Institute of Advanced Engineering and Science, 2026)
This document presents a deep learning-based approach for the automatic classification of brain tumors from magnetic resonance imaging (MRI) images, using the lightweight MobileNetV2 model combined with transfer learning and fine-tuning techniques. The study aims to address the constraints of resource-limited medical environments, where model speed and lightweight design are as important as accuracy. The dataset used is a fusion of three public databases (Figshare, SARTAJ, and BR35H), comprising 4,480 images for training and 1,600 for testing, divided into four classes: glioma, meningioma, pituitary tumor, and healthy brain. The methodology includes several steps: resizing the images to 224×224 pixels, normalizing pixel values between 0 and 1, augmenting the data through random rotations, shifts, and zooms to avoid overfitting, and then extracting features using MobileNetV2 pre-trained on ImageNet. The strategy adopted comprises two phases: first, transfer learning where only the layers added at the top of the model are trained for 10 epochs, then fine-tuning consisting of unfreezing the last 20 layers of the base model and retraining them with a reduced learning rate for 5 epochs. The results obtained show an overall accuracy of 86.56% after fine-tuning, with a macro-mean area under the ROC curve (AUC) of 0.9645, indicating excellent discriminatory power. The confusion matrix reveals that the