VGG16 and ResNet50 for Brain Tumor Classification: Comparative Performance and Grad-CAM++ Analysis

Vollständiger Abstract

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Reliable categorization of brain tumors from magnetic resonance imaging (MRI) is a prerequisite for timely clinical intervention, yet the great majority of transfer-learning studies in this space report a single backbone in isolation, leaving open the question of how much of the reported performance is attributable to the chosen architecture rather than to dataset or training choices. This paper presents a controlled comparative evaluation of two ImageNet-pretrained convolutional backbones, VGG16 and ResNet50, for four-class brain tumor classification (glioma, meningioma, pituitary tumor, no tumor) on a merged corpus of 7,200 MRI slices drawn from the Figshare, SARTAJ, and Br35H repositories. Both backbones are fitted with an identical classification head and trained under an identical two-phase (frozen, then partially fine-tuned) protocol, so that any accuracy differential can be attributed to the backbone rather than to confounding methodological variation. On a held-out test set of 1,600 images, VGG16 attains 94.31% accuracy with a weighted F1-score of 94.20%, while ResNet50 attains 94.00% accuracy with a weighted F1-score of 93.89%; VGG16 is accordingly identified as the better-performing architecture under the present experimental conditions. To move beyond an opaque class label, Grad-CAM++ is applied to the selected model to generate a visual attribution map for each classification decision, highlighting the image regions that most strongly influenced the predicted tumor class. The resulting comparative and interpretability findings are intended as a controlled, architectureisolated reference point for subsequent work in this line of research rather than as a claim of universally superior performance.