region:us
# Brain Tumor Classification Model

## Overview
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.

## Model Architecture
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.

## Dataset
The dataset used can be accessed through the following link: [Brain Tumor MRI Dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset).

## Training
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.

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

## Test Evaluation
Average Validation Loss: 0.1160, Average Validation Accuracy: 0.9527

## Final Evaluation
Average Validation Loss: 0.0893, Average Validation Accuracy: 0.9658
