View in EDS HTML Full Text PDF Full Text

Enriched dermoscopic-structure-based cad system for melanoma diagnosis.

Saved in:
Bibliographic Details
Title: Enriched dermoscopic-structure-based cad system for melanoma diagnosis.
Authors: López-Labraca, Javier1 jlabraca@tsc.uc3m.es, Fernández-Torres, Miguel Ángel1 matorres@tsc.uc3m.es, González-Díaz, Iván1 igonzalez@tsc.uc3m.es, Díaz-De-María, Fernando1 fdiaz@tsc.uc3m.es, Pizarro, Ángel2 apizarro@ricardoruiz.es
Source: Multimedia Tools & Applications. May2018, Vol. 77 Issue 10, p12171-12202. 32p.
Subjects: Melanoma diagnosis, Computer diagnostic software, Bayesian analysis, CAD/CAM systems, Skin cancer diagnosis
Abstract: Computer-Aided Diagnosis (CAD) systems for melanoma detection have received a lot of attention during the last decades because of the utmost importance of detecting this type of skin cancer in its early stages. However, despite of the many research efforts devoted to this matter, these systems are not used yet in everyday clinical practice. Very likely, this is due to two main reasons: 1) the accuracy of the systems is not high enough; and 2) they simply provide a parallel diagnosis that actually does not help to the doctors (as long as there is no way to interpret it). In this paper, we propose a novel approach that aims to provide the doctor with an enriched diagnosis. Specifically, we rely on a dermoscopic-structure-based soft segmentation to design a set of structure-specific classifiers. Each individual structure-specific classifier is trained to distinguish benign lesions from melanomas just paying attention to one type of dermoscopic structure. Then, the outputs of the individual classifiers are combined by a means of the Bayesian method that, besides the final diagnosis, provide the doctor with additional valuable information, such as the opinions of the individual structure-specific experts and the uncertainty of the diagnosis. The results in terms of the features selected for the structure-specific classifiers are consistent with the expert insights. Furthermore, regarding the automatic melanoma diagnosis problem, the proposed method has been assessed on two different datasets, and the experimental results revealed that the proposed system clearly outperforms other methods in two datasets and compares well with the official submissions of the ISBI 2016 challenge on melanoma detection. Moreover, the system performance is equivalent to that of a well-known dermoscopy expert and its combination with the human diagnosis surpasses the human performance. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 129795128
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Enriched dermoscopic-structure-based cad system for melanoma diagnosis.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22López-Labraca%2C+Javier%22">López-Labraca, Javier</searchLink><relatesTo>1</relatesTo><i> jlabraca@tsc.uc3m.es</i><br /><searchLink fieldCode="AR" term="%22Fernández-Torres%2C+Miguel+Ángel%22">Fernández-Torres, Miguel Ángel</searchLink><relatesTo>1</relatesTo><i> matorres@tsc.uc3m.es</i><br /><searchLink fieldCode="AR" term="%22González-Díaz%2C+Iván%22">González-Díaz, Iván</searchLink><relatesTo>1</relatesTo><i> igonzalez@tsc.uc3m.es</i><br /><searchLink fieldCode="AR" term="%22Díaz-De-María%2C+Fernando%22">Díaz-De-María, Fernando</searchLink><relatesTo>1</relatesTo><i> fdiaz@tsc.uc3m.es</i><br /><searchLink fieldCode="AR" term="%22Pizarro%2C+Ángel%22">Pizarro, Ángel</searchLink><relatesTo>2</relatesTo><i> apizarro@ricardoruiz.es</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. May2018, Vol. 77 Issue 10, p12171-12202. 32p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Melanoma+diagnosis%22">Melanoma diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+diagnostic+software%22">Computer diagnostic software</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22CAD%2FCAM+systems%22">CAD/CAM systems</searchLink><br /><searchLink fieldCode="DE" term="%22Skin+cancer+diagnosis%22">Skin cancer diagnosis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Computer-Aided Diagnosis (CAD) systems for melanoma detection have received a lot of attention during the last decades because of the utmost importance of detecting this type of skin cancer in its early stages. However, despite of the many research efforts devoted to this matter, these systems are not used yet in everyday clinical practice. Very likely, this is due to two main reasons: 1) the accuracy of the systems is not high enough; and 2) they simply provide a parallel diagnosis that actually does not help to the doctors (as long as there is no way to interpret it). In this paper, we propose a novel approach that aims to provide the doctor with an enriched diagnosis. Specifically, we rely on a dermoscopic-structure-based soft segmentation to design a set of structure-specific classifiers. Each individual structure-specific classifier is trained to distinguish benign lesions from melanomas just paying attention to one type of dermoscopic structure. Then, the outputs of the individual classifiers are combined by a means of the Bayesian method that, besides the final diagnosis, provide the doctor with additional valuable information, such as the opinions of the individual structure-specific experts and the uncertainty of the diagnosis. The results in terms of the features selected for the structure-specific classifiers are consistent with the expert insights. Furthermore, regarding the automatic melanoma diagnosis problem, the proposed method has been assessed on two different datasets, and the experimental results revealed that the proposed system clearly outperforms other methods in two datasets and compares well with the official submissions of the ISBI 2016 challenge on melanoma detection. Moreover, the system performance is equivalent to that of a well-known dermoscopy expert and its combination with the human diagnosis surpasses the human performance. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=129795128
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s11042-017-4879-3
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 32
        StartPage: 12171
    Subjects:
      – SubjectFull: Melanoma diagnosis
        Type: general
      – SubjectFull: Computer diagnostic software
        Type: general
      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: CAD/CAM systems
        Type: general
      – SubjectFull: Skin cancer diagnosis
        Type: general
    Titles:
      – TitleFull: Enriched dermoscopic-structure-based cad system for melanoma diagnosis.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: López-Labraca, Javier
      – PersonEntity:
          Name:
            NameFull: Fernández-Torres, Miguel Ángel
      – PersonEntity:
          Name:
            NameFull: González-Díaz, Iván
      – PersonEntity:
          Name:
            NameFull: Díaz-De-María, Fernando
      – PersonEntity:
          Name:
            NameFull: Pizarro, Ángel
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 05
              Text: May2018
              Type: published
              Y: 2018
          Identifiers:
            – Type: issn-print
              Value: 13807501
          Numbering:
            – Type: volume
              Value: 77
            – Type: issue
              Value: 10
          Titles:
            – TitleFull: Multimedia Tools & Applications
              Type: main
ResultId 1