Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

By T. Brinker, A. Hekler, J. Utikal, Niels Grabe, D. Schadendorf, J. Klode, C. Berking, T. Steeb, Alexander Enk, C. von Kalle. Journal of Medical Internet Research, 2018

424 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Epidemiology, Oncology

View Paper: https://www.semanticscholar.org/paper/99e9ecdde6308835c34df6784c724510a83ff591

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Citations in the corpus

Skin Cancer Detection: A Review Using Deep Learning Techniques (2021, background): “For accurate skin cancer detection in darkskinned people, a neural network must learn to account for skin color [76].”
Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review (2019, background): “AI-based classification of physical characteristics via vast numbers of examples is reinforced during training, and this ability is consolidated and gradually levels the discriminative academic performance in appearance-based diagnoses such as skin diseases [15,21].”
Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network (2022, background): “It covers the complete body and its thickness differs significantly over all parts of the body, and also varies between man and woman and the old and young one [1], [2].”