The Open Dermatology Journal




ISSN: 1874-3722 ― Volume 12, 2018
REVIEW ARTICLE

A Review of Prevalent Methods for Automatic Skin Lesion Diagnosis



Damilola A Okuboyejo*, Oludayo O Olugbara
ICT and Society Research Group, Durban University of Technology, Durban, South Africa

Abstract

Background:

Skin cancer has been reported to be one of the most predominant forms of cancer diseases, especially amongst Caucasian descendant and light-skinned people. In particular, the melanocytic skin lesion has been judged to be the most deadly amongst three prevalent skin cancer diseases and the second most common form amongst young adults ranging from 15-29 years of age. These concerns have propelled the need to provide automated systems for medical diagnosis of skin cancer diseases within a strict time window towards reducing the unnecessary biopsy, increasing the speed of diagnosis and providing reproducibility of diagnostic results.

Objective:

This paper is aimed at using a comparative analysis method to review and compare the existing novel approaches for automating the diagnostic procedures of melanocytic skin lesion, including their success and shortcomings. This task is particularly valuable for decision makers to consider tradeoffs inaccuracy of diagnostic procedure versus complexity.

Methods:

A comparative study was carried out on selected literature from different accessible digital libraries of skin lesion research, especially cancerous moles in regard to the convention used, assumptions made, success recorded and noticeable gaps that need to be adequately filled by further study.

Conclusion:

Image standardization should be embraced in the medical research community to ensure the reproducibility of findings. Moreover, efforts should be made to have a large image library of varying skin lesion samples with categories based on lesion types and making these accessible to researchers to ensure proper benchmarking of research results.

Keywords: Computer-assisted dermoscopy, Skin lesion segmentation, Pattern recognition, Remote health diagnosis, Medical image analysis, Computational intelligence, Melanoma skin disease, Automated diagnosis.


Article Information


Identifiers and Pagination:

Year: 2018
Volume: 12
First Page: 14
Last Page: 53
Publisher Id: TODJ-12-14
DOI: 10.2174/187437220181201014

Article History:

Received Date: 14/11/2017
Revision Received Date: 06/12/2017
Acceptance Date: 27/3/2018
Electronic publication date: 30/04/2018
Collection year: 2018

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© 2018 Okuboyejo and Olugbara.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the ICT and Society Research Group, Durban University of Technology, P.O. Box 1334, 4000, Durban, South Africa; E-mail: d.okuboyejo@gmail.com


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