Gliomas Disease Prediction Using CNN and Image Processing Techniques

Gliomas are a life-threatening form of brain tumor, and early diagnosis is important to enhance patient outcomes.This project describes an automated approach based on Convolutional Neural Networks (CNN) and image processing methods for glioma detection from MRI scans.MRI datasets were obtained from public sources and hospitals, followed by preprocessing like noise reduction, contrast enhancement, and segmentation to enhance image quality.A CNN model was trained to classify tumors, and its performance was measured using accuracy, precision, recall, and F1-score.The system seeks to aid radiologists by saving diagnostic time and errors, as well as providing a scalable deep learning framework that can be used to extend to other medical imaging tasks.