LesionIQ skin cancer diagnostic

LesionIQ uses machine learning to analyse images of skin lesions and identify patterns that the human eye cannot. LesionIQ skin cancer diagnostic. Summer is now at its peak (in the Northern Hemisphere) but, while most of us enjoy basking in the sun, it comes with an inherent risk.

The number of diagnoses of basal cell carcinoma, the most common skin cancer, continues to rise while cases of melanoma skin cancer – the most dangerous of them all – are reaching record highs. “According to The Skin Cancer Foundation, skin cancer is the most common cancer globally,” says scientist Tess Watt. “Despite this, if melanoma is detected and treated at an early stage, its five-year survival rate is 99%.” A photograph is taken of a skin lesion using an interface designed to be as user-friendly as possible.

The system is a demonstration of TinyML – machine learning running on lower-powered, non-connected devices With this in mind, Tess, a PhD candidate in the School of Mathematical and Computer Sciences at Heriot-Watt University in Edinburgh, has created an early detection system. She’s developed a set of AI tools designed to diagnose skin cancer and other skin conditions using a Raspberry Pi 3 Model B computer attached to a small camera. The aim has been to produce a low-cost device that can be used by patients living in remote areas of the world.

It allows skin conditions to be monitored from home without the need for an internet connection.