Deep Learning Approach for Brain Tumor Classification, Segmentation, and Stage Detection
Kishore S
Department Of Artificial Intelligence and Data Science
Panimalar Institute Of Technology Chennai, India kdkishore315@gmail.com
Harivignesh B
Department Of Artificial Intelligence and Data Science
Panimalar Institute Of Technology Chennai, India hariprathiirah220903@gmail.com
Karthi B
Department Of Artificial Intelligence and Data Science
Panimalar Institute Of Technology Chennai, India rossikarthi2911@gmail.com
Mrs. Vidhya Muthulakshmi
Assistant Professor, Department of Artificial Intelligence and Data science
Panimalar Institute Of Technology Chennai, India vidhyamuthulakshmi@gmail.com
Dr. Kalai Chelvi
Professor and Head of Department, Department of Artificial Intelligence and Data science
Panimalar Institute Of Technology Chennai, India tkalaichelvi@panimalar.ac.in
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.
