Literature DB >> 29845364

[Image-based computer diagnosis of melanoma].

V Dick1, P Tschandl1, C Sinz1, A Blum2, H Kittler3.   

Abstract

The use of automated diagnostic systems for the diagnosis of melanomas is becoming increasingly more established. These are based on the following four steps: 1) preprocessing, to ensure that disturbing factors are eliminated, 2) segmentation, the separation of the image and the background, 3) extraction and selection of features that provide the highest measure of accuracy for the diagnosis and 4) classification, in which the lesion is assigned to a diagnostic class. Recently, the computer-assisted diagnosis of melanoma has focused on algorithms based on transfer learning, which can make steps 2 and 3 obsolete and provides better results. In this article we also review smartphone applications in the field of melanoma screening and recognition. These applications should be considered with caution as they are available to lay persons although the diagnostic accuracy of these applications has not usually been tested in clinical trials.

Entities:  

Keywords:  Classification; Dermatoscopy; Machine learning; Mobile applications; Skin neoplasms

Mesh:

Year:  2018        PMID: 29845364     DOI: 10.1007/s00105-018-4191-9

Source DB:  PubMed          Journal:  Hautarzt        ISSN: 0017-8470            Impact factor:   0.751


  17 in total

Review 1.  Diagnostic accuracy of dermoscopy.

Authors:  H Kittler; H Pehamberger; K Wolff; M Binder
Journal:  Lancet Oncol       Date:  2002-03       Impact factor: 41.316

2.  Accuracy of computer diagnosis of melanoma: a quantitative meta-analysis.

Authors:  Barbara Rosado; Scott Menzies; Alexandra Harbauer; Hubert Pehamberger; Klaus Wolff; Michael Binder; Harald Kittler
Journal:  Arch Dermatol       Date:  2003-03

3.  A comparison of classification methods as diagnostic system: A case study on skin lesions.

Authors:  Suhail M Odeh; Abdel Karim Mohamed Baareh
Journal:  Comput Methods Programs Biomed       Date:  2016-09-23       Impact factor: 5.428

Review 4.  Computerized analysis of pigmented skin lesions: a review.

Authors:  Konstantin Korotkov; Rafael Garcia
Journal:  Artif Intell Med       Date:  2012-10-11       Impact factor: 5.326

5.  Diagnostic inaccuracy of smartphone applications for melanoma detection.

Authors:  Joel A Wolf; Jacqueline F Moreau; Oleg Akilov; Timothy Patton; Joseph C English; Jonhan Ho; Laura K Ferris
Journal:  JAMA Dermatol       Date:  2013-04       Impact factor: 10.282

6.  Mortality burden and prognosis of thin melanomas overall and by subcategory of thickness, SEER registry data, 1992-2013.

Authors:  Shoshana M Landow; Annie Gjelsvik; Martin A Weinstock
Journal:  J Am Acad Dermatol       Date:  2016-11-22       Impact factor: 11.527

7.  A comparison of machine learning methods for the diagnosis of pigmented skin lesions.

Authors:  S Dreiseitl; L Ohno-Machado; H Kittler; S Vinterbo; H Billhardt; M Binder
Journal:  J Biomed Inform       Date:  2001-02       Impact factor: 6.317

8.  Challenges to smartphone applications for melanoma detection.

Authors:  Jordan V Wang; Lance W Chapman; Matthew Keller
Journal:  Dermatol Online J       Date:  2017-02-15

Review 9.  [Dermoscopy for malignant and benign skin tumors : Indication and standardized terminology].

Authors:  A Blum; J Kreusch; W Stolz; H Haenssle; R Braun; R Hofmann-Wellenhof; P Tschandl; I Zalaudek; H Kittler
Journal:  Hautarzt       Date:  2017-08       Impact factor: 0.751

Review 10.  Computational methods for the image segmentation of pigmented skin lesions: A review.

Authors:  Roberta B Oliveira; Mercedes E Filho; Zhen Ma; João P Papa; Aledir S Pereira; João Manuel R S Tavares
Journal:  Comput Methods Programs Biomed       Date:  2016-04-08       Impact factor: 5.428

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  1 in total

1.  [Dermatoscopy-30 years after the First Consensus Conference].

Authors:  Andreas Blum; Friedrich A Bahmer; Jürgen Bauer; Ralph P Braun; Brigitte Coras-Stepanek; Teresa Deinlein; Thomas Eigentler; Christine Fink; Claus Garbe; Holger A Haenssle; Rainer Hofmann-Wellenhof; Harald Kittler; Jürgen Kreusch; Hubert Pehamberger; Hans Schulz; H Peter Soyer; Wilhelm Stolz; Philipp Tschandl; Iris Zalaudek
Journal:  Hautarzt       Date:  2019-11       Impact factor: 0.751

  1 in total

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