While you most likely don’t realize it, machine learning is often used in your daily life. For example, when social media suggests tagging your friends in pictures because it recognizes them, or the spam filter on your email account removing unwanted emails. In healthcare, machine learning also takes its part in recognizing skin cancer. Machine learning has been used in hospitals for many years, but now you can use it yourself to track your health in the comfort of your home!
Over the past years, Skinive has made great progress towards developing an application that is significantly reliable in recognizing dangerous skin lesions. Skinive started off with a ‘rule-based’ system which went through every picture and checked skin lesions for certain characteristics to determine risk. Even though this algorithm has helped us detect the risk of thousands of dangerous lesions, we are continuously looking to improve its accuracy.
That is why we are very excited to announce that in August 2018 we introduced a smart system that detects dangerous skin lesions completely based on a machine learning algorithm.
What is Machine Learning?
Here is a great animation that explains it in two minutes.
How is Machine Learning used in the Skinive application?
We have trained the Skinive algorithm with large quantities of images which were previously assessed by our team of dermatologists. The algorithm learns which lesions are dangerous and which ones are not. We continuously train and improve our algorithm with new sets of images. From now on, all the pictures submitted through the Skinive application go through this algorithm.
It is common for doctors to ask a second opinion, and so at this moment, every photo is also reviewed by our in-house dermatologists and image recognition experts. We have set up this process to assist the algorithm to become more accurate and to make sure that our dermatologists agree with the risk indication. The best part, however, is that we are training our algorithm to become on a par with the best dermatologists.
Every photo of skin spots makes our algorithm smarter at detecting skin cancer risk. Contribute to our mission of saving lives by using Skinive . Try it now!
Recommended articles preview:
1st June 2010
[AI Generated] What Is a Maculopapular Rash?
OpenAI NN input: Overview A maculopapular rash is made of both flat and raised skin lesions. The name is a blend of the words “macule,” which are flat discolored skin lesions, and “papule,” which are small raised bumps. These skin lesions are usually red and can merge together. Macules that are bigger than 1 centimeter […]
1st June 2010
[AI Generated] Overview of skin flushing
OpenAI NN input: Skin flushing or blushing describes feelings of warmth and rapid reddening of your neck, upper chest, or face. Blotchiness or solid patches of redness are often visible when blushing. Flushing happens as a result of increased blood flow. Whenever there is more blood flow to an area of skin (such as your […]
14th February 2020
Skinive at the Rockstart AI Ecosystem Day in the Academy of Data Science.
On February 12th, investors, entrepreneurs, mentors, scientists and interested people gathered in the beautiful campus of the Jheronimus Academy of Data Science (JADS) in ‘s-Hertogenbosch (The Netherlands). They hosted the Rockstart AI Ecosystem Day for the 3rd time. Rockstart AI is an organization supporting artificial intelligence and emerging technology companies and startups in Europe. As such, they […]