Brain Tumor Classification using CNN

Accurate identification of brain tumors from magnetic resonance imaging (MRI) plays an important role in clinical diagnosis and treatment planning. This paper presents a deep learning–based method for automated brain tumor classification using a Convolutional Neural Network (CNN). The proposed CNN model is trained from scratch on a publicly available brain MRI dataset containing four classes: glioma, meningioma, pituitary tumor, and no tumor. All images are resized to a uniform resolution and processed through an end-to-end learning framework without applying explicit data augmentation. The network learns relevant spatial features through convolutional and pooling layers, followed by fully connected layers for multi-class classification. Experimental results show that the proposed CNN achieves a test accuracy of about 95%, with balanced class-wise performance reflected by a macro-averaged precision, recall, and F1-score of 96%. These findings indicate that CNN-based models can effectively learn meaningful tumor characteristics from MRI scans and may serve as a useful tool to support computer-aided brain tumor diagnosis.