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Review ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Med. | doi: 10.3389/fmed.2019.00191

The possibility of deep learning-based, computer-aided skin tumor classifiers

  • 1University of Tsukuba, Japan

The incidence of skin tumors has steadily increased. Although most are benign and do not affect survival, some of the more malignant skin tumors present a lethal threat if a delay in diagnosis permits them to become advanced. Ideally, an inspection by an expert dermatologist would accurately detect malignant skin tumors in the early stage; however, it is not practical for every single patient to receive intensive screening by dermatologists. To overcome this issue, many studies to develop dermatologist-level, computer-aided diagnostics are being developed. Whereas many systems that can classify dermoscopic images at this dermatologist-equivalent level have been reported, a much fewer number of systems that can classify conventional clinical images have been reported thus far. Recently, the introduction of deep-learning technology, a method that automatically extracts a set of representative features to use for further classification has dramatically improved classification efficacy. This new technology has the potential to improve the computer classification accuracy of conventional clinical images to the level of skilled dermatologists. In this review, this new technology and present development of computer-aided skin tumor classifiers will be summarized.

Keywords: artificial intelligence, deep learning, Convolutional Neural Network, Clinical image, Dermoscopy, skin tumor classifier

Received: 12 Jun 2019; Accepted: 13 Aug 2019.

Copyright: © 2019 Fujisawa, Inoue and Nakamura. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Yasuhiro Fujisawa, University of Tsukuba, Tsukuba, Japan,