Breast Cancer Detection in ML with Web End Deployment using Flask
[https://breast-cancer-prediction-flask.herokuapp.com/]
Predicts whether the type of breast cancer is Malignant or Benign
Breast cancer is the most common type of cancer in women. When cancers are found early, they can often be cured. There are some devices that detect the breast cancer but many times they lead to false positives, which results is patients undergoing painful, expensive surgeries that were not even necessary. These type of cancers are called benign which do not require surgeries and we can reduce these unnecessary surgeries by using Machine Learning. We take a dataset of the previous breast cancer patients and train the model to predict whether the cancer is benign or malignant. These predictions will help doctors to do surgeries only when the cancer is malignant, thus reducing the unnecessary surgeries for woman.
For building the project we have used Wisconsin Breast cancer data which has 569 rows of which 357 are benign and 212 are malignant. The data is prepossessed and scaled. We have trained both ANN and SVM and SVM gives best accuracy of 98.2%. To provide the easy to use interface to doctors we have developed a website that will take the data and display the output with accuracy and time taken to predict.
- Python: language
- NumPy: library for numerical calculations
- Pandas: library for data manipulation and analysis
- SkLearn: library which features various classification, regression and clustering algorithms
- Flask: microframework for building web applications using Python.
To use this project, clone the repo
git clone https://github.com/vaibhavhariaramani/Breast_Cancer_Detection_ML-with-Web-End-Deployment.git
After cloning, using the command prompt, go to the project directory and run python app.py to start the web server. Now you can go to link which was displayed in command prompt to access the web application. Firstly, make sure you have installed all necessary libraries or packages installed on your system.
To learn more about these Resources you can Refer to some of these articles written by Me:-
https://sites.google.com/view/geeky-traveller/computer-vision/facial-landmarks-detection
if you use this repo in your project don't forget to mention us as Contributer in it . And Don't forget to tag us Linkedin, instagram, facebook , twitter, Github
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I am a Machine Learning enthusiast, an Actions on Google Developer, Internet of things, Alexa Skills, and Image processing developer. I have a keen interest in Image processing and Andriod development. I am Currently studying at Chandigarh University, Punjab.
My PortFolio You can find me at:- Linkedin or Github .
Email: vaibhav.hariramani01@gmail.com
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