BRAIN TUMOR DETECTION USING CONVOLUTIONAL NEURAL NETWORK

This project presents the development of an automated brain tumor detection system using deep learning techniques to analyze MRI images and classify them into tumor and non-tumor categories. A convolutional neural network (CNN) was utilized to automatically extract   relevant features from the images, improving accuracy and reducing manual effort. The system follows a complete pipeline including preprocessing (resizing, normalization, and augmentation), model training, validation, and deployment for real-time prediction through a user-friendly interface. The trained model achieved high accuracy and demonstrated reliable performance on unseen data. As shown in the output, the system correctly predicted “No Tumor” for the given MRI image with a confidence score of 99.54%, indicating strong confidence in its classification. Overall, the project provides an efficient, accurate, and scalable solution for brain tumor detection, with potential applications in assisting medical professionals in early diagnosis and clinical decision-making, while future improvements can further enhance performance using larger and more diverse datasets. sophisticated deep learning models, and improving security and interpretability of the predictions for improved trust and scalability on production environments

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