PROBLEM:

Skin disorders vary greatly in symptoms and severity. They can be temporary or permanent, and may be painless or painful. Some have situational causes, while others may be genetic. Some skin conditions are minor, and others can be life-threatening.

Eg, 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.

The main goal is to develop a technology that helps the dermatologist in the diagnosis of common skin disorders

SOLUTION:

With this Medical System, we want to use the power of artificial intelligence to provide early detection as widely as available. Using Artificial Intelligence we can help to detect and classify most common skin diseases such as: cancer, black fungus, acne, cold sore, carbuncle, psoriasis, cellulitis, melanoma, etc.

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 start to build nevus, melanoma, and seborrheic keratosis image classifier using deep learning algorithm, the Convolution Neural Network (CNN) through Caffe Framework.

Eg, my first goal is to build a machine learning algorithm that can detect cancer images in real time, this way we can build our own AI based skin cancer classification device.

Our application will include two 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.

Potential consumers of this product: Hospitals, Medical Research Centers, Universities, Dermatologists