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It’s common for us to read news stories and articles about the rising number of skin cancer cases, especially within the United Kingdom. The UK’s health service actually spends more then £100 million every year diagnosing and treating individual cases of skin cancer on British patients, but all this looks set to change.
Along with the technology boom comes the revolution of using computer, tablet and laptop applications and smartphone apps to keep an eye on your personal health. Now, a team of scientists and app developers have teamed up at Romania’s University of Bucharest to create an application known as SkinVision, which in turn is providing everyday people with a new weapon against skin cancer.
SkinVision has the incredible ability to detect melanoma, with an accuracy scoring of an incredible 73% using only a photo. Although the app is in no way a replacement for a healthcare professional or dermatologist, the app is designed to detect early warning signs of melanoma and increase awareness of the condition amongst the general public. We might not all have time to visit a doctor with busy lifestyles, but with an app on our phones it becomes a lot easier to keep an eye on our personal health.
SkinVision is ingeniously designed around a technology known as ‘fractal geometry’, a field of mathematics which can be used to simulate the natural growth of human tissue. The SkinVision team in Bucharest used fractal geometry to develop a special algorithm which specifically analyses tissue growth using just a picture of a mole which can be quickly uploaded to a computer or smartphone. Using this, the algorithm can detect skin cancer symptoms which are usually associated with moles and other skin characteristics linked to melanoma.
SkinVision co-founder Mircea Popa told Business Insider about the app: “The skin is made up by cells that have replicated over and over again by following a rule. That leads to patterns that are measureable in a mathematical way. Malignant cells are diverted cells and they replicate in a different way, so the patterns get mixed up – signalling an abnormality.”