Machine Learning-Based Brain Tumor Detection and Classification Using Deep Convolutional Neural Networks

Author(s)	Dr. Rama Mohan Y, Dr. Anantha Lakshmi V, Dr. Satheesh G, Dr. Kishor G
Country	India
Abstract	Early and accurate detection of brain tumors from Magnetic Resonance Imaging (MRI) plays a pivotal role in improving clinical diagnosis and patient survival rates. Manual review of large volumes of 2D MRI slices is labor-intensive and prone to human error. In this paper, we present an automated machine learning framework for the binary detection (Tumor vs. Non-Tumor) and multi-class classification (Glioma, Meningioma, Pituitary, and No Tumor) of brain lesions using deep Convolutional Neural Networks (CNNs) combined with classical machine learning classifiers. By applying preprocessing techniques including spatial resizing, intensity normalization, and data augmentation, our proposed model achieves a classification accuracy of 97.5%, outperforming traditional machine learning baselines such as Support Vector Machines (SVM) and Random Forest (RF). Furthermore, we incorporate Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize high-attention spatial features, providing a lightweight, interpretable, and highly reliable computer-aided diagnostic tool for clinical environments
Keywords	Brain Tumor Detection, Image Classification, Deep Learning, Convolutional Neural Networks (CNN), Support Vector Machines, Grad-CAM, Medical Imaging, Feature Extraction.
Field	Engineering
Published In	Volume 7, Issue 4, July-August 2026
Published On	2026-08-01

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Advanced International Journal for Research