Multi-Class Brain Tumour Classification using Convolutional Neural Network
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Abstract
In recent years, the subject of medical image categorization has expanded significantly, with Convolutional Neural Networks (CNN) being the most popular neural network model for this purpose. Using components like convolutional layers, pooling layers, and fully connected layers, CNNs are built to learn features adaptively through backpropagation. This study aims to develop and deploy a CNN model for identifying brain cancers using MRI(Magnetic resonance image) scans obtained from Kaggle. The proposed Methodology consists of two phases: preprocessing the pictures using various image processing techniques and then training a CNN model to categorise the modified images. We test the model on 3,264 MRI pictures representing four kinds of brain cancers (glioma, meningioma, no tumor, and pituitary). Results from the experiment showed a training dataset accuracy of 99.39% and a validation dataset accuracy of 96.18%.
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