Recent advancement in the early detection of melanoma using computerized tools: An image analysis perspective.


Journal

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
ISSN: 1600-0846
Titre abrégé: Skin Res Technol
Pays: England
ID NLM: 9504453

Informations de publication

Date de publication:
Mar 2019
Historique:
accepted: 23 06 2018
pubmed: 22 7 2018
medline: 14 8 2019
entrez: 22 7 2018
Statut: ppublish

Résumé

The paper reviews the advancement of tools and current technologies for the detection of melanoma. We discussed several computational strategies from pre- to postprocessing image operations, descriptors, and popular classifiers to diagnose a suspected skin lesion based on its virtual similarity to the malignant lesion with known histopathology. We reviewed the current state of smart phone-based apps as diagnostic tools for screening. A literature survey was conducted using a combination of keywords in the bibliographic databases: PubMed, AJCC, PH2, EDRA, and ISIC melanoma project. A number of melanoma detection apps were downloaded for two major mobile operating systems, iOS and Android; their important uses, key challenges, and various expert opinions were evaluated and also discussed. We have provided an overview of research on the computer-aided diagnosis methods to estimate melanoma risk and early screening. Dermoscopic images are the most viable option for the advent of new image processing technologies based on which many of the skin cancer detection apps are being developed recently. We have categorized and explored their potential uses, evaluation criteria, limitations, and other details. Such advancements are helpful in the sense they are raising awareness. Diagnostic accuracy is the major issue of smart phone-based apps and it cannot replace an adequate clinical experience and biopsy procedures.

Sections du résumé

BACKGROUND BACKGROUND
The paper reviews the advancement of tools and current technologies for the detection of melanoma. We discussed several computational strategies from pre- to postprocessing image operations, descriptors, and popular classifiers to diagnose a suspected skin lesion based on its virtual similarity to the malignant lesion with known histopathology. We reviewed the current state of smart phone-based apps as diagnostic tools for screening.
METHODS METHODS
A literature survey was conducted using a combination of keywords in the bibliographic databases: PubMed, AJCC, PH2, EDRA, and ISIC melanoma project. A number of melanoma detection apps were downloaded for two major mobile operating systems, iOS and Android; their important uses, key challenges, and various expert opinions were evaluated and also discussed.
RESULTS RESULTS
We have provided an overview of research on the computer-aided diagnosis methods to estimate melanoma risk and early screening. Dermoscopic images are the most viable option for the advent of new image processing technologies based on which many of the skin cancer detection apps are being developed recently. We have categorized and explored their potential uses, evaluation criteria, limitations, and other details.
CONCLUSION CONCLUSIONS
Such advancements are helpful in the sense they are raising awareness. Diagnostic accuracy is the major issue of smart phone-based apps and it cannot replace an adequate clinical experience and biopsy procedures.

Identifiants

pubmed: 30030916
doi: 10.1111/srt.12622
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

129-141

Informations de copyright

© 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Auteurs

Nivedita Singh (N)

Department of Bioinformatics, Systems Toxicology Group, CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India.
Department of Biochemistry, Babu Banarasi Das University, Lucknow, Uttar Pradesh, India.

Shailendra K Gupta (SK)

Department of Bioinformatics, Systems Toxicology Group, CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India.
Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India.

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