This repository contains a deep learning model for classifying brain tumor images into different categories using convolutional neural networks (CNNs). The model is trained on a dataset consisting of MRI images of brain tumors.

The model architecture used for this classification task is a convolutional neural network (CNN). The CNN consists of multiple convolutional layers followed by max-pooling layers to extract features from the input images. The extracted features are then passed through fully connected layers to perform classification.

The dataset used can be accessed through the following link: Brain Tumor MRI Dataset.

The model is trained using the PyTorch deep learning framework. The training process involves optimizing the model parameters using the Adam optimizer and minimizing the categorical cross-entropy loss function. Training is performed on a GPU for faster computation.

The model is evaluated using various metrics such as accuracy. These metrics provide insights into the model's performance in classifying brain tumor images.

Average Validation Loss: 0.1160, Average Validation Accuracy: 0.9527

Average Validation Loss: 0.0893, Average Validation Accuracy: 0.9658