Colorectal Cancer Detection Using CNN

Table of Contents

1. Introduction
2. Features
3. Requirements
4. Installation
5. Usage
6. License

Introduction

This project leverages Convolutional Neural Networks (CNN) to detect colorectal cancer using medical image datasets. The application uses TensorFlow for model training and Django for building a web-based interface for prediction.

Features

• Image upload for cancer prediction.
• Web-based UI for model interaction.
• TensorFlow-based backend for accurate predictions.

Requirements

Before running the project, ensure you have the following installed:

• Python 3.8 or higher
• Conda (Anaconda/Miniconda)
• TensorFlow 2.15.0

Installation

Follow these steps to set up the project on your local machine:

1. Clone the Repository:

   git clone https://github.com/SurrajKumar2000/Lung-and-Colon-Cancer-prediction-using-CNN.git
   
   

2. Create and Activate a Virtual Environment: Use Conda to create a virtual environment and activate it:

   conda create --name colorectal-cancer-detection python=3.8 -y
   conda activate colorectal-cancer-detection
   

3. Install Project Dependencies: Once the virtual environment is activated, install all the required dependencies. If you have a requirements.txt file, you can install the dependencies using the following command:

    pip install -r requirements.txt
   

4. Run the Server: To start the Django development server and run the project locally, use the following command:

   python manage.py runserver
   

5. Access the Application: Once the server is running, open your web browser and navigate to the following URL to access the application:

   http://127.0.0.1:8000/
   

6. Colorectal Cancer Detection Project Video

You can watch the video demonstration of this project by clicking the link below:

bandicam.2024-12-14.05-13-44-316.-.Trim.mp4