Invasive Ductal Carcinoma (IDC) Classifier
This project shows how existing deep learning technologies can be utilized to train artificial intelligence (AI) to be able to detect invasive ductal carcinoma (IDC)1 (breast cancer) in unlabeled histology images. More specifically, I show how to train a convolutional neural network using TensorFlow* and transfer learning using a dataset of negative and positive histology images. In addition to showing how artificial intelligence can be used to detect IDC, I also show how the Internet of Things (IoT) can be used in conjunction with AI to create automated systems that can be used in the medical industry.
The IDC Classifier is made up of 3 projects that combine together to make a network:
This is a project I created as an extension to one of my facial recognition projects, I advise that this is to be used by developers interested in learning about the use cases of computer vision, medical researchers and students, or professionals in the medical industry to evaluate if it may help them and to expand upon. This is not meant to be an alternative for use instead of seeking professional help. I am a developer not a doctor or expert on cancer.
- Uses code from Intel® movidius/ncsdk (movidius/ncsdk Github)
- Uses code from chesterkuo imageclassify-movidius (imageclassify-movidius Github)
- Uses data from paultimothymooney on Kaggle (predict-idc-in-breast-cancer-histology-images data on Kaggle)
Please feel free to create issues for bugs and general issues you come across whilst using this or any other IoT JumpWay Intel repo issues: IoT-JumpWay-Intel-Examples Github Issues.