AI Powered Brain Tumor Detection

Abstract

Brain tumors are one of the critical medical conditions that require early and accurate detection for effective treatment planning. Magnetic Resonance Imaging (MRI) is one of the most commonly used techniques for detecting brain abnormalities because it provides detailed images of brain tissues. However, manual analysis of MRI scans requires expert radiologists and is a time-consuming process. The chances of human error and delays in diagnosis create a need for an automated and efficient detection system. This project presents an AI Powered Brain Tumor Detection Using Convolutional Neural Network (CNN) that uses Artificial Intelligence and Deep Learning techniques to automatically detect brain tumors from MRI images. The proposed system analyzes MRI brain scans and classifies them into two categories: Brain Tumor Detected and No Brain Tumor Detected. The system consists of various stages including image acquisition, image preprocessing, CNN model development, training, testing, and result prediction. The input MRI images are first preprocessed by resizing and normalization techniques to improve image quality and make them suitable for model processing. The Convolutional Neural Network automatically extracts important features such as edges, textures, and abnormal patterns from MRI images. These extracted features are then used for classification. The CNN model is trained using labeled MRI image datasets containing tumor and non-tumor samples. During testing, the trained model analyzes new MRI images and provides prediction results The proposed system demonstrates the application of Artificial Intelligence in the healthcare domain by providing an automated, accurate, and reliable approach for medical image analysis. Overall, the AI Powered Brain Tumor Detection Using CNN system provides an effective solution for supporting ear