<![CDATA[Brain tumors are a major global health burden, and Magnetic Resonance Imaging (MRI) is the primary modality for their detection. Manual interpretation of MRI is time-consuming and subject to inter-observer variability, motivating automated classification using Convolutional Neural Networks (CNNs). A recurring weakness in existing studies is the comparison of CNN architectures and optimizers using single-run accuracy without formal statistical testing, leaving it unclear whether reported differences are genuine or due to random training variation. This study presents a statistically validated comparison of four CNN architectures (AlexNet, VGG-16, InceptionV3, and MobileNetV2) for four-class brain tumor classification (glioma, meningioma, pituitary, and no tumor) on the Kaggle Brain Tumor MRI dataset (7,023 images). AlexNet was trained from scratch, whereas the other three used feature-extraction transfer learning with frozen ImageNet-pretrained backbones. All models were evaluated using 5-fold stratified cross-validation, and architecture differences were assessed using one-way ANOVA with Tukey HSD post-hoc testing and Cohen's d effect sizes. The best architecture was then optimized with Adam and Stochastic Gradient Descent with Momentum (SGDM), compared using a paired t-test at the 0.05 level. AlexNet achieved the highest mean test accuracy of 96.63%, significantly outperforming VGG-16 (92.60%), InceptionV3 (88.89%), and MobileNetV2 (88.25%), with large effect sizes. Adam and SGDM produced statistically equivalent accuracy (97.38% versus 96.52%). The results indicate that learning domain-specific features from scratch can surpass non-fine-tuned transfer learning, and that the choice between Adam and SGDM does not significantly affect accuracy.]]>
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