This research project leverages deep learning techniques to develop a robust system for the classification of brain tumors from MRI scans. At its core, the project utilizes a Convolutional Neural Network (CNN) trained to classify MRI images into four categories: Glioma Tumor, Meningioma Tumor, Pituitary Tumor, and No Tumor. This multiclass classification approach aims to support medical professionals by providing an automated and accurate solution for brain tumor diagnosis.

The workflow involves an efficient image preprocessing pipeline, including resizing and normalization of MRI images, to prepare the data for optimal model performance. The CNN architecture is meticulously designed to extract spatial patterns and complex features from the MRI scans, enabling precise classification of tumor types.

The research emphasizes the integration of advanced deep learning techniques with medical imaging, achieving high accuracy and reliability in tumor classification. This report outlines the architecture, preprocessing techniques, and performance analysis of the model, demonstrating its effectiveness in addressing the challenges of multiclass brain tumor detection. The project also explores opportunities for future enhancements, such as expanding the dataset and adapting the model to 3D imaging for even greater diagnostic utility.