Future of Machine Learning in Diagnosis of Skin Diseases

Moles, psoriasis, hives, eczema, and recently associated Covid-19 coronavirus rashes are just a few of the more than 3,000 skin disorders known to dermatology. But now, medical field believes that Machine Learning and Artificial Intelligence can be used to support clinicians by Image Classification using Deep Neural Networks.

The basic process is that the clinician will add an image, the machine will recognize the morphology of the skin disease, and produce a differential diagnosis or a prediction.

The expected benefits of this process are:

  • Faster and better diagnosis = Faster and better treatment
  • Improve knowledge and skills in primary care – Fewer errors
  • Reduce costs – Fewer visits, tests, treatments, and referrals

How will it work?

This is how humans learn. We hear a name, we recognize that the name is associated with a particular object, and when we see the object again, we can classify the object.

So it is with Machine Learning. The machine recognized a Melanoma, and it recognized the non-Melanoma as well. When the machine has shown another Melanoma, it can classify accordingly.

Artificial Intelligence is advancing. According to a research in Germany, the US and France trained a deep learning neural network to identify skin cancer by feeding it with more than 100,000 images of malignant melanomas.

After its training, they compared its performance with 58 international dermatologists. While the dermatologists accurately detected more than 86% of the melanomas, the neural network detected 95% of it.

What’s needed?

  • Big Data that is correctly labelled
  • Machine Learning Software
  • Technology Experts

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