Image-processing techniques in machine learning (ML) for skin cancer detection using clinical images.

By combining those tools with machine learning, their goal was to see if the combination would work as a noninvasive and more objective in vivo diagnostic tool.

In this study, researchers used machine learning and combination theory to distil 22 clinical features down to the seven most important that predict if a skin lesion might be suspicious or not.

LUGANO, 30 September, 2021 – A new study has found that a direct-to-consumer machine learning model for detecting skin cancers incorrectly classified rare and aggressive cancers as low-risk.1 ...

Despite advances in artificial intelligence and machine learning, such technologies have not been optimized by dermatologists and other professionals for skin cancer detection, according to a ...

A sub-analysis of the study highlighted that the DermaSensor device effectively detects skin cancer across different skin tones.

New biomarkers to improve skin cancer detection and avoid delays in treatment are being developed by researchers at the University of South Australia.

Machine learning has the potential to save thousands of people from skin cancer each year—while putting others at greater risk.