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Usage of Deep Learning in skin cancer detection

Authors: Russinger, Miriam;

Usage of Deep Learning in skin cancer detection

Abstract

Image recognition is an important topic when it comes to skin cancer detection, since the incidence of skin cancer is rising sharply, worldwide, and the detection of pigmented lesions is still a challenge for dermatologists. The field constitutes an interface between dermatology and computer science and combines conventional detection methods with KI systems. Studies have shown that convolutional networks cause good outcomes in skin cancer detection due to its handling with big data, an AUC at 99% with a ResNet-152 was measured. The diagnostic accuracy is impressive and can be compared to a dermatologysts decision. However, there are some limitations, e.g. restricted training data, that need to be considered and also further explored, therefore at the moment it can be only used as an assisting tool.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Cancer Research