Deep Learning-Based Brain Tumor Detection System

Abstract

Brain tumor detection is one of the most important tasks in the medical field because early diagnosis can significantly improve patient survival rates and treatment effectiveness. Magnetic Resonance Imaging (MRI) is commonly used for identifying abnormalities in brain tissues; however, manual analysis of MRI images is time-consuming and highly dependent on radiologists’ expertise. To address these challenges, this paper presents an automated Brain Tumor Detection System using Deep Learning techniques. The proposed system uses Convolutional Neural Networks (CNN) along with Transfer Learning based on the VGG16 architecture to classify MRI brain images into Tumor and No Tumor categories. The MRI images are preprocessed using resizing, normalization, and data augmentation techniques to improve model accuracy and robustness. The deep learning model automatically extracts important features from MRI images and performs efficient classification with minimal human intervention. A Flask-based web application is developed to provide real-time prediction results through a simple and user-friendly interface. The experimental results demonstrate that the proposed system achieves high accuracy, reduces diagnostic time, and minimizes human errors. The system can assist healthcare professionals in faster decision-making and can be further extended for advanced tumor classification and real-time medical applications.