Automated detection of melanocytes related pigmented skin lesions

A clinical framework

Sameena Pathan, K. Gopalakrishna Prabhu, P. C. Siddalingaswamy

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A clinically oriented Computer-Aided Diagnostic (CAD) system is of prime importance for the diagnosis of melanoma, since the deadly disease is associated with high morbidity and mortality. Unfortunately, the development of CAD tools is hampered by several issues, such as (i) smooth boundaries between the lesion and the surrounding skin, (ii) subtlety of features between the melanoma and non-melanoma skin lesions, and (iii) lack of reproducibility of CAD systems due to complexity. The proposed system aims to address the aforementioned issues. First, the lesion regions are localized by incorporating chroma based deformable models. Second, the lesion patterns are analyzed to detect various dermoscopic criteria. Further, a robust ensemble architecture is developed using dynamic classifier selection techniques to detect malignancy. Quantitative analysis is performed on two diverse datasets (ISBI and PH2) achieving an accuracy of 88% and 97%, sensitivity of 95% and 97% and specificity of 82% and 100% for ISBI and PH2 datasets respectively.

Original languageEnglish
Pages (from-to)59-72
Number of pages14
JournalBiomedical Signal Processing and Control
Volume51
DOIs
Publication statusPublished - 01-05-2019

Fingerprint

Melanocytes
Skin
Melanoma
Classifiers
Morbidity
Mortality
Chemical analysis
Neoplasms
Datasets

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Health Informatics

Cite this

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title = "Automated detection of melanocytes related pigmented skin lesions: A clinical framework",
abstract = "A clinically oriented Computer-Aided Diagnostic (CAD) system is of prime importance for the diagnosis of melanoma, since the deadly disease is associated with high morbidity and mortality. Unfortunately, the development of CAD tools is hampered by several issues, such as (i) smooth boundaries between the lesion and the surrounding skin, (ii) subtlety of features between the melanoma and non-melanoma skin lesions, and (iii) lack of reproducibility of CAD systems due to complexity. The proposed system aims to address the aforementioned issues. First, the lesion regions are localized by incorporating chroma based deformable models. Second, the lesion patterns are analyzed to detect various dermoscopic criteria. Further, a robust ensemble architecture is developed using dynamic classifier selection techniques to detect malignancy. Quantitative analysis is performed on two diverse datasets (ISBI and PH2) achieving an accuracy of 88{\%} and 97{\%}, sensitivity of 95{\%} and 97{\%} and specificity of 82{\%} and 100{\%} for ISBI and PH2 datasets respectively.",
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Automated detection of melanocytes related pigmented skin lesions : A clinical framework. / Pathan, Sameena; Gopalakrishna Prabhu, K.; Siddalingaswamy, P. C.

In: Biomedical Signal Processing and Control, Vol. 51, 01.05.2019, p. 59-72.

Research output: Contribution to journalArticle

TY - JOUR

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AU - Gopalakrishna Prabhu, K.

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AB - A clinically oriented Computer-Aided Diagnostic (CAD) system is of prime importance for the diagnosis of melanoma, since the deadly disease is associated with high morbidity and mortality. Unfortunately, the development of CAD tools is hampered by several issues, such as (i) smooth boundaries between the lesion and the surrounding skin, (ii) subtlety of features between the melanoma and non-melanoma skin lesions, and (iii) lack of reproducibility of CAD systems due to complexity. The proposed system aims to address the aforementioned issues. First, the lesion regions are localized by incorporating chroma based deformable models. Second, the lesion patterns are analyzed to detect various dermoscopic criteria. Further, a robust ensemble architecture is developed using dynamic classifier selection techniques to detect malignancy. Quantitative analysis is performed on two diverse datasets (ISBI and PH2) achieving an accuracy of 88% and 97%, sensitivity of 95% and 97% and specificity of 82% and 100% for ISBI and PH2 datasets respectively.

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