Enhanced Brain Tumor Classification Using Optimized Convolutional Neural Networks: A Deep Learning Approach

In brain tumors, both benign and malignant tumors significantly compromise health and call for accurate diagnosis and classification for the best therapy. This work uses Convolutional Neural Networks (CNNs) and Deep Learning’s ability to improve the accuracy of brain tumor categorization. Variations in CNN design are investigated to maximize model performance on a dataset including 7,523 MRI pictures categorized as glioma, meningioma, pituitary tumor, and non-tumor categories. The CNN model significantly improved accuracy by varying the learning rate and changing dropout and flattening layers; it reached up to 99% while lowering loss to as low as 0.0039. This work emphasizes the possibilities of DL in medical imaging, especially for automated brain tumor categorization, therefore providing a strong instrument for early and accurate diagnosis.