Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Mohamed A Kassem; Khalid M Hosny; Robertas Damaševičius; Mohamed Meselhy Eltoukhy

doi:10.3390/diagnostics11081390

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Diagnostics (Basel). 2021 Jul 31;11(8):1390. doi: 10.3390/diagnostics11081390.

Authors

Mohamed A Kassem¹, Khalid M Hosny², Robertas Damaševičius³, Mohamed Meselhy Eltoukhy⁴

Affiliations

¹ Department of Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kaferelshiekh University, Kaferelshiekh 33511, Egypt.
² Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt.
³ Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania.
⁴ Computer Science Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt.

Abstract

Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

Keywords: deep learning; machine learning; racial bias; skin image segmentation; skin lesion classification; small data.

Publication types

Review