Year: 2025
Type: article
Abstract: I Brain tumors are among the most critical medical conditions, requiring precise and timely detection for effective treatment. Traditional methods of diagnosis, such as MRI scans analyzed by radiologists, are time-consuming and prone to human error. To overcome these limitations, deep learning techniques have emerged as a powerful solution for automating tumor classification, segmentation, and stage detection. This project implements a convolutional neural network (CNN)-based approach to classify brain tumors into benign and malignant categories, segment tumor regions accurately, and predict their severity. The system is trained on a dataset of MRI images, utilizing image preprocessing techniques and deep learning architectures to enhance accuracy. By integrating this approach into a desktop application, we ensure accessibility, efficiency, and real-time analysis for medical professionals, thereby improving diagnostic reliability and patient outcomes. Keywords- Deep Learning, Brain Tumor Classification, Tumor Segmentation, Stage Detection, Convolutional Neural Networks (CNN), Medical Image Processing, MRI Analysis, Computer-Aided Diagnosis, Neural Networks, Automated Tumor Detection.
DOI: 10.55041/ijsrem48343
Author: Kishore S
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4410526442