Ultra96 Skin Cancer AI Detection

Why Did We Build Skin Cancer AI?

According to the Skin Cancer Foundation, half of the population in United States are diagnosed with some form of skin cancer by age 65. The survival rate for early detection is almost 98%, but it falls to 62% when the cancer reaches the lymph node and 18% when it metastasizes to distance organs. With Skin Cancer AI, we want to use power of artificial intelligence to provide early detection as widely as available.

Ultra96 Skin Cancer AI Detection

What Is AI and How to Use It?

Deep learning has been a pretty big trend for machine learning lately, and the recent success has paved the way to build project like this. We are going to focus specifically on computer vision and image classification in this sample. To do this, we will be building nevus, melanoma, and seborrheic keratosis image classifier using deep learning algorithm, the Convolution Neural Network (CNN) through Caffe Framework.

In this article we will focus on Supervised learning, it requires training on the server as well as deploying on the edge. Our goal is to build a machine learning algorithm that can detect cancer images in real time, this way you can build your own AI based skin cancer classification device.

Our application will include 2 parts, the first part is training, which we will be using different sets of cancer image database to train a machine learning algorithm (model) with their corresponding labels. The second part is deploying on the edge, which uses the same model we’ve trained and running it on an Edge device, in this case Movidius Neural Computing Stick through Ultra96 FPGA. This way VPU can run inference while FPGA can do the OpenCV

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