Brain Tumor Classification Project

This project aims to classify brain tumors from medical imaging data using a deep learning model. The goal is to accurately identify different types of brain tumors, potentially assisting in medical diagnosis.

Project Overview

This repository contains the code, trained model, evaluation results, and data source information for a brain tumor classification project. The project involves:

• Data Preprocessing: Preparing medical images for training.
• Model Building: Developing a convolutional neural network (CNN) using TensorFlow/Keras.
• Training: Training the model on a labeled dataset of brain tumor images.
• Evaluation: Assessing the model's performance using metrics like accuracy, precision, recall, and F1-score.

Repository Contents

• _results_files/: This directory contains visualization files generated during the project, including:
  • confusion_matrix.png: A visual representation of the model's classification performance.
  • accuracy_plot.png: A plot showing the training and validation accuracy over epochs.
  • Other relevant plots or visualizations.
• Data.txt: This file contains a link to the dataset used for this project.
• Model_1.h5: This file is the saved, trained deep learning model in the HDF5 format. It can be loaded and used for making predictions on new brain scan images.
• README.md: This file (the one you are currently reading) provides an overview of the project and its contents.

Data Source

The dataset used for training and evaluating this brain tumor classification model can be found at the following link:

[https://www.kaggle.com/datasets/thomasdubail/brain-tumors-256x256]

Please refer to the dataset's documentation for details on its structure, licensing, and usage guidelines.

Model

The trained deep learning model is saved in the Model_1.h5 file. This model can be loaded using TensorFlow/Keras:

from tensorflow.keras.models import load_model

loaded_model = load_model('Model_1.h5')