Effective treatment planning and early diagnosis depend on the detection and classification of brain tumors. The ability of Convolutional Neural Networks (CNNs) to automatically extract pertinent information from images has made them extremely effective in medical image analysis in recent years. In this work, a CNN-based technique for classifying and detecting brain cancers from MRI data is presented. The suggested model classifies cancers into various categories, such as gliomas, meningiomas, and pituitary tumors, and accurately detects their presence using a deep learning framework. Preprocessing MRI images, training the CNN model on a large dataset of labeled brain scans, and evaluating the model's performance using metrics like accuracy, sensitivity, and specificity are all part of the process. Experimental findings demonstrate how well the suggested approach detects and classifies brain tumors with high accuracy and dependability, making it a useful tool to aid radiologists in clinical decision-making.
Python, Machine Learning (ML), Deep Learning, CNN, Computed Tomography(CT), MRI, Artificial Intelligence (AI), Medical image processing.
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