Classifying Brain Tumors using Advanced Machine Learning Techniques: A Comprehensive Comparative Analysis

Brain tumors, which develop uncontrollably, are difficult to diagnose, treat, and prognose. Treatment and outcomes depend on early detection and precise classification. This study uses imaging and deep learning advances to compare three state-of-the-art Convolutional Neural Network (CNN) brain tumor classification models: VGG16, InceptionV3, and ResNet50. The Kaggle dataset comprises meningioma, glioma, pituitary tumors, and no tumors. VGG16 was the best performer, with 0.89 accuracy, 0.91 precision, 0.89 recall, and 0.90 F 1 -score, thanks to its simple architecture that captures crucial features. ResNet50 used residual learning to solve the vanishing gradient problem, achieving 0.86 accuracy and a 0.87 F1-score. Despite its novel multi-scale feature extraction, InceptionV3 has an accuracy of 0.74 and an $\mathbf{F 1}$-score of 0.75, possibly due to overfitting. These findings demonstrate VGG16’s applicability for brain tumor detection clinical applications demanding high precision and recall. Advanced data augmentation, regularization, and ensemble methods should be studied to improve complex models like InceptionV3. These CNN architectures could be optimized for clinical brain tumor classification using larger and more diversified datasets. Early intervention and constant monitoring are crucial to brain tumor management and patient quality of life, according to this study.