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Prediction of Breast Cancer using histopathology image
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A Convolutional Neural Network, also known as CNN or ConvNet, is a class of neural networks that specializes in processing data that has a grid-like topology, such as an image. The benefits of deep learning in healthcare are plentiful – fast, efficient, accurate – but they don’t stop there. Even more benefits lie within the neural networks formed by multiple layers of AI
and ML
and their ability to learn. With the help of these great technologies, I have created a model that could predict whether a person is malignant with Breast Cancer or not.
Breast cancer is an uncontrolled growth of breast cells.
Why Breast Cancer is dangerous:
- 1 in 8 women who live to be age 70 will develop breast cancer in her lifetime.
- Breast cancer is the second most common cancer in women in Ireland.
- In 2020, there were 2.3 million women diagnosed with breast cancer and 685 000 deaths globally WHO
- In 2022, it is estimated that 43,250 women and 530 men will die of breast cancer. ACS
But there is good news:
- Breast Cancer's mortality rate has been declining since 1989
- Women whose breast cancer is detected at an early stage have a 93 percent or higher survival rate
Based on the experimental results, the proposed model achieved 90% accuracy
Here is the list of frameworks and libraries used to create this project.
System Information OS-Windows11, Processor-AMD Ryzen 5 5500U, Graphics-Radeon Graphics, Physical Memory (RAM) 8.00 GB
To get a local copy up and running follow these simple steps.
Clone the Repo.
git clone https://github.com/ACM40960/project-RaghulRavikumar.git
Below is the list of applications that are needed to be installed to view and execute this project locally.
Frameworks
- Anaconda (Steps to Install)
⚠️ select python version above 3.5 - Atom (Steps to Install)
Launch the terminal by following the below steps
- Execute Anaconda Navigator
- Click on Environments
- Create an Environment
- Select created environment -> Open Terminal
Now you are good to install the required packages using console. Use the below commands to install them in your environment.
To Execute the Model
conda install numpy
conda install pandas
conda install matplotlib
conda install seaborn
conda install scikit-learn
pip install pytest-shutil
pip3 install opencv-python
conda install tensorflow
To Render with UI
pip install streamlit
pip install pybase64
pip install datetime
pip install Pillow
To Train The Model
- Open Anaconda Navigator
- Launch Jupyter Notebook Note: Please Install if require
- Upload "Second version.ipynb"
- Press Shift+Enter to run cells one by one More information about the model are given in the file
To View as Web Application
- Launch Anaconda terminal as mentioned here
- Navigate to the location were the project is cloned using cd command
- Make sure
information.py
file is located and file structure is maintained - Now use below command to host the application locally
streamlit run information.py
Once the webpage is launched click on the Predict tab
- Step 1: Fill the User Information (Not Mandatory)
- Step 2: Upload The Image
- Step 2.1: Browse File to upload the histopathology image
- Step 2.2: You can also use sample image if you don't have any by selecting Use Sample Image
- Step 3: Click "Run On This Image"
- Step 4: Please Wait
You can see the result for the uploaded image
For detailed steps with screenshot, please refer to the How-To-Use
- Improve Accuracy of the Model (Current Accuracy at 92%)
- Include Login Functionality
- Introduce Database to store user information
- Fix Issues if any
- Mobile Application Development
- Android
- iOS
If you have a suggestion that would make this project better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature
) - Commit your Changes (
git commit -m 'Add some AmazingFeature'
) - Push to the Branch (
git push origin feature/AmazingFeature
) - Open a Pull Request
This Application is currently not licensed and is free to use by everyone.
Your Name - Sukesh Perla - [email protected]
Project Link: Breast Cancer Detection
Use this space to list resources you find helpful and would like to give credit to. I've included a few of my favorites to kick things off!