- This application detect whether the Mass in the breast is cancerous or not using Artificial Neural Network.
- Its uses measurement of mass present is the report for analyzing the mass.
- The patient can use this web application as a second opinion to confirm diagnosis.
Python Version: 3.7
Packages: pandas, numpy, matplotlib, sklearn, tensorflow , flask, json, pickle
For Web Framework Requirements: pip install -r requirements.txt
Dataset is downloaded from kaggle.com The data in in data.csv. The number of columns is 30 and the number of rows is 570. The columns are: For more detail info please visit : https://www.kaggle.com/uciml/breast-cancer-wisconsin-data
First, I have scaled the data with MinMaxScaler. I also split the data into train and tests sets with a test size of 20%.
I have applied Artificial Neural Network (ANN) with four layers:
- Input Layer – Units=30, activation=relu
- Hidden Layer – Units=10, activation=relu
- Hidden Layer – Units=10, activation=relu
- Output Layer – Units=1, activation=binary crossentropy
I have also applied early stopping.
- Validation loss : 0.0115
- Validation Accuracy : 0.995 or 99.5%
- **Confusion matrix ** : [[64 3] [ 3 44]]
In this step, I built a flask API endpoint that is hosted on Heroku. The API endpoint takes in a request with a list of values of breast mass measurements and returns whether the mass is cancerous or not.
Application link: https://breastcancer-detector.herokuapp.com/
