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An application for breast cancer detection. The app can tell whether the breast mass is benign(non-cancerous cells) or malignant(cancerous cells).
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is a Python3 project to classify cancer data using Google's TensorFlow library and Neural Networks. The goal of this project was to validate and demonstrate that modern machine learning techniques in neural nets could prove to be useful in classifying cancer datasets.
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Link of the dataset used: http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29 and http://portals.broadinstitute.org/cgi-bin/cancer/datasets.cgi
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This repo contains dnn_data_classifier - A Deep Neural Network implementation to classify breast cancer tumours as benign or malignant depending on measurements taken directly from tumours.
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The motivation for applying neural nets at cancer in particular came from Cancer Research's Citizen Science. This is a project that relied on volunteers to classify images of breast cancer tumours. The images themselves contained a mixture of different looking cells. Despite having over 2,000,000 contributions, the project struggled to differentiate cancer cells from non-cancer cells. Relying on volunteers to manually classify cancer seemed both inefficient and ineffective, and I believed that neural nets could provide a better method for classifying cancer.
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The attributes in the sample are in the range (1-10). So, doctors will enter the values of attributes and clicking on the compute button can tell whether it's benign or malignant.
- Python >=2.7.14
- TensorFlow >= 1.4.0
- Flask
- Heroku