Computerised image analysis of vitiligo lesion: evaluation using manually defined lesion areas

Nugroho, Hanung Adi; Fadzil, M. H. Ahmad; Shamsudin, Norashikin; Hussein, Suraiya H.

doi:10.1111/j.1600-0846.2011.00610.x

Cited by 23 publications

(23 citation statements)

References 11 publications

(9 reference statements)

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“…15 The system, however, needs to be commercialised as end-user software suitable for daily clinical practice. C-DIAS should also be tested on photographs taken with other non-SLR digital camera models.…”

Section: Discussionmentioning

confidence: 99%

“…Further validation tests of C-DIAS, in which the technique was compared with manually segmented vitiligo skin lesions, show a high rate of sensitivity (91%), specificity (100%) and accuracy (99%). 15 The system, however, needs to be commercialised as end-user software suitable for daily clinical practice. C-DIAS should also be tested on photographs taken with other non-SLR digital camera models.…”

Section: Discussionmentioning

confidence: 99%

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Objective assessment of vitiligo with a computerised digital imaging analysis system

et al. 2014

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Out of 56 patients, 44 (79%) responded. Overall, the response was mild in 22 (39%), moderate in 21(40%) and good in one (2%) patient(s). A total of 39 (70%) patients responded as measured by PGA. The repigmentation was mild in 27(48%), moderate in 10 (18%) and good to excellent in two (4%) patients. The κ test of consistency was 0.17 (P = 0.053), which shows poor agreement between the two assessment methods, although this is not statistically significant. The C-DIAS can be used to perform an objective analysis of repigmentation or depigmentation in vitiligo skin lesions in response to treatment.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Objective assessment of vitiligo with a computerised digital imaging analysis system

et al. 2014

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“…Other limitations found in the literature are single disease classification and training on limited data ( Ganster et al, 2001;Nugroho et al, 2013;Giotis et al, 2015;Jain et al, 2015;Alam et al, 2016;). Continuing with the single disease classification, Dorj et al used SVM classifier on the features obtained by the Convolutional Neural Network (CNN) to classify the skin cancer.…”

Section: Related Workmentioning

confidence: 99%

Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques

Hameed

Shabut

Ghosh

et al. 2020

Expert Systems with Applications

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Skin diseases remain a major cause of disability worldwide and contribute approximately 1.79% of the global burden of disease measured in disability-adjusted life years. In the United Kingdom alone, 60% of the population suffer from skin diseases during their lifetime. In this paper, we propose an intelligent digital diagnosis scheme to improve the classification accuracy of multiple diseases. A Multi-Class Multi-Level (MCML) classification algorithm inspired by the "divide and conquer" rule is explored to address the research challenges. The MCML classification algorithm is implemented using traditional machine learning and advanced deep learning approaches. Improved techniques are proposed for noise removal in the traditional machine learning approach. The proposed algorithm is evaluated on 3672 classified images, collected from different sources and the diagnostic accuracy of 96.47% is achieved. To verify the performance of the proposed algorithm, its metrics are compared with the Multi-Class Single-Level classification algorithm which is the main algorithm used in most of the existing literature. The results also indicate that the MCML classification algorithm is capable of enhancing the classification performance of multiple skin lesions.

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“…Therefore, a device that covers a wide area is required to assist the objective evaluation of dermatologists , and a standardized high‐quality photograph can produce an accurate evaluation . Quantitative evaluation of the pigmentation is possible by involving multiple image processing techniques , and it allows dermatologist to make consistent and objective evaluations .…”

Section: Introductionmentioning

confidence: 99%

Development and clinical validation of a novel photography‐based skin pigmentation evaluation system: a comparison with the calculated consensus of dermatologists

Cho

Lee

Kim

et al. 2016

Intern J of Cosmetic Sci

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Computerised image analysis of vitiligo lesion: evaluation using manually defined lesion areas

Abstract: With the proposed method, physicians are able to assess vitiligo treatment efficacies objectively.

Cited by 23 publications

References 11 publications

Objective assessment of vitiligo with a computerised digital imaging analysis system

Objective assessment of vitiligo with a computerised digital imaging analysis system

Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques

Development and clinical validation of a novel photography‐based skin pigmentation evaluation system: a comparison with the calculated consensus of dermatologists

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