A new research project at Stanford University is promising to bring things up to a professional grade of diagnosis, through a deep learning algorithm that can detect potential cancers with the same accuracy as dermatologists in early tests.
‘Advances in computer-aided classification of benign versus malignant skin lesions could greatly assist dermatologists in improved diagnosis for challenging lesions.’
The researchers built a database of almost 130,000 images of skin lesions representing more than 2,000 different diseases. They then took a Google-developed algorithm designed to distinguish cats from dogs, and adapted it to their skin cancer problem by feeding it each image as raw pixels and an accompanying disease label.
"There's no huge dataset of skin cancer that we can just train our algorithms on, so we had to make our own," said Brett Kuprel, co-lead author of the paper. "We gathered images from the internet and worked with the medical school to create a nice taxonomy out of data that was very messy - the labels alone were in several languages, including German, Arabic and Latin."
The team then had 21 trained dermatologists diagnose cancerous and non-cancerous lesions from over 370 images. It then put its new algorithm to the test, tasking it with identifying the most common skin cancers, and then separately identifying the deadliest of skin cancers: malignant melanomas.
"We realized it was feasible, not just to do something well, but as well as a human dermatologist," said Sebastian Thrun, an adjunct professor in the Stanford Artificial Intelligence Laboratory. "
The authors speculated that with the explosive growth of smartphones, everyday people could be given tools to spot and identify skin issues early.
Smartphones "can therefore potentially provide low-cost universal access to vital diagnostic care".
Reference
- Andre Esteva et al., Dermatologist-level classification of skin cancer with deep neural networks, Nature (2017)
doi:10.1038/nature21056.
Source: Medindia
