Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review
Journal of Medical Internet Research2018Vol. 20(10), pp. e11936–e11936
Citations Over TimeTop 1% of 2018 papers
Titus J. Brinker, Achim Hekler, Jochen Utikal, Niels Grabe, Dirk Schadendorf, Joachim Klode, Carola Berking, Theresa Steeb, Alexander Enk, Christof von Kalle
Abstract
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.
Related Papers
- → Measuring the Comparability of Company Accounts Conditionally: A Research Note(2017)10 cited
- → Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation(2020)2 cited
- A Study of Comparability of Nature Quantity Cent in Different Criterion Districts of Farmland Classification ——with Henan Province as an example(2005)
- → Towards Comparability in Non-Intrusive Load Monitoring: On Data and\n Performance Evaluation(2020)