Brain tumors are among the most critical neurological disorders that require early and accurate diagnosis for effective treatment. This study presents a deep learning-based approach for brain tumor classification using Convolutional Neural Networks (CNN). The proposed system analyzes MRI images and classifies them into glioma, pituitary, meningioma, and no tumor categories. The methodology includes preprocessing techniques such as image resizing, normalization, and data augmentation to improve model performance. A multi-layer CNN architecture is used to extract hierarchical features from MRI images. The model is trained using the Adam optimizer and categorical cross-entropy loss function. Experimental results demonstrate that the proposed model achieves an accuracy of 94.3%, outperforming traditional machine learning techniques such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). The system provides an efficient and reliable solution to assist medical professionals in accurate and faster diagnosis.
Rana et al. (2026) studied this question.