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AUTOMATED BRAIN TUMOR DETECTION USING IMAGE PROCESSING TECHNIQUE
Brain tumors are a serious health problem, and detecting them early is very important for successful treatment. Normally, doctors confirm tumors through surgery and biopsy, which are invasive methods. In this project, artificial intelligence (AI) is used to provide a non-invasive way of diagnosis. By using MRI brain scans, deep learning models are developed to classify gliomas, meningiomas, pituitary tumors, and healthy brain tissues. The main model used is a Convolutional Neural Network (CNN), which learns patterns from a large dataset of MRI images. To improve accuracy, preprocessing techniques such as normalization, scaling, and data augmentation are applied. These steps make the system stronger and reduce false positives and false negatives. The proposed model shows high accuracy in separating tumor and non-tumor cases, helping doctors make faster and more reliable decisions. This project demonstrates how AI can support medical professionals, reduce manual effort, and provide precise forecasts. Overall, it highlights the importance of automated systems in medical imaging, making diagnosis more efficient, trustworthy, and useful for early detection of brain tumors.
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