Skin cancer classification using convolutional neural networks

• Verfasst von: Brinker, Titus Josef [VerfasserIn]
• Verfasst von: Utikal, Jochen [VerfasserIn]
• Verfasst von: Grabe, Niels [VerfasserIn]
• Verfasst von: Enk, Alexander [VerfasserIn]
• Verfasst von: Kalle, Christof von [VerfasserIn]
• Titel: Skin cancer classification using convolutional neural networks
• Titelzusatz: systematic review
• Verf.angabe: Titus Josef Brinker, Achim Hekler, Jochen Sven Utikal, Niels Grabe, Dirk Schadendorf, Joachim Klode, Carola Berking, Theresa Steeb, Alexander H. Enk, Christof von Kalle
• E-Jahr: 2018
• Jahr: 17.10.18
• Umfang: 8 S.
• Fussnoten: Gesehen am 24.09.2019
• Titel Quelle: Enthalten in: Journal of medical internet research
• Ort Quelle: Richmond, Va. : Healthcare World, 1999
• Jahr Quelle: 2018
• Band/Heft Quelle: 20(2018,10) Artikel-Nummer e11936, 8 Seiten
• ISSN Quelle: 1438-8871
• DOI: doi:10.2196/11936
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 167840151X

Abstract

Background: State-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermatologists and could enable lifesaving and fast diagnoses, even outside the hospital via installation of apps on mobile devices. To our knowledge, at present there is no review of the current work in this research area. Objective: This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future. Methods: We searched the Google Scholar, PubMed, Medline, ScienceDirect, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review. Results: We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large dataset and then optimize its parameters to the classification of skin lesions are the most common ones used and they display the best performance with the currently available limited datasets. Conclusions: CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use nonpublic datasets for training and/or testing, thereby making reproducibility difficult. Future publications should use publicly available benchmarks and fully disclose methods used for training to allow comparability. [J Med Internet Res 2018;20(10):e11936]