https://github.com/adadalshabab/brain-tumor-classification-using-conv2d-layer

Cutting-edge repository for Brain Tumor Classification leveraging Conv2D layers. Implements state-of-the-art deep learning techniques for accurate medical image analysis. Enhance diagnostic precision with this advanced convolutional neural network solution.
https://github.com/adadalshabab/brain-tumor-classification-using-conv2d-layer

cnn convolutional-neural-networks deep-learning neural-network

Last synced: about 1 month ago
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Cutting-edge repository for Brain Tumor Classification leveraging Conv2D layers. Implements state-of-the-art deep learning techniques for accurate medical image analysis. Enhance diagnostic precision with this advanced convolutional neural network solution.

• Host: GitHub
• URL: https://github.com/adadalshabab/brain-tumor-classification-using-conv2d-layer
• Owner: AdadAlShabab
• Created: 2023-11-25T06:43:07.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-07-12T17:19:43.000Z (6 months ago)
• Last Synced: 2024-07-12T19:38:52.671Z (6 months ago)
• Topics: cnn, convolutional-neural-networks, deep-learning, neural-network
• Homepage:
• Size: 476 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Brain Tumor Classification Using Conv2D Layer

This repository contains an implementation of a Convolutional Neural Network (CNN) model for the classification of brain tumor images. The model is built using Conv2D layers and trained on a dataset of brain MRI scans to accurately classify images into tumor and non-tumor classes.

## Overview

Brain tumor classification is a critical task in medical image analysis, aiding in diagnosis and treatment planning. This project focuses on utilizing deep learning techniques to automate this classification process, providing a reliable and efficient tool for medical professionals.

## Features

- **Conv2D Layers:** The core of the model architecture consists of Conv2D layers, which are well-suited for image classification tasks, allowing the network to learn hierarchical features.

  

- **Dataset:** The model is trained on a carefully curated dataset comprising brain MRI scans with labeled tumor and non-tumor regions, ensuring robust performance and generalization.

- **Classification:** The trained model is capable of accurately classifying new brain MRI images into tumor and non-tumor categories, enabling quick and reliable diagnosis.

## Requirements

- Python (>=3.6)

- TensorFlow (>=2.0)

- NumPy

- Pandas

- Keras

- Numpy

- Pillow

- Other dependencies as specified in `requirements.txt`

## Usage

1. **Clone the Repository:**

   ```bash

   git clone https://github.com/AdadAlShabab/Brain-Tumor-Classification-Using-Conv2D-Layer.git

   cd Brain-Tumor-Classification-Using-Conv2D-Layer

   ```

2. **Install Dependencies:**

   ```bash

   pip install -r requirements.txt

   ```

## Contribution

Contributions are welcome! If you have any suggestions, improvements, or feature requests, feel free to open an issue or submit a pull request.

## Acknowledgments

- The implementation of this project was inspired by various works in medical image analysis and deep learning.

- Special thanks to the creators and maintainers of the datasets used in this project.

## References

Include any references to academic papers, articles, or resources used in developing this project.

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Feel free to reach out if you have any questions or need further assistance!