Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma

Rajpara, Sanjay; Botello, A P; Townend, John; Ormerod, AD

doi:10.1111/j.1365-2133.2009.09093.x

Cited by 144 publications

(85 citation statements)

References 88 publications

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“…In general, ADD programs analyze an imaged lesion for malignancy-associated features that are weighted, summed and then compared to a predetermined (usually experimentally-obtained) cut-off score to differentiate between benign and malignant lesions. Two meta-analyses comparing the accuracy of 'expert-implemented' dermoscopy to ADD found no significant difference between the two [39,40]. While sensitivity for melanoma between expert dermoscopists and ADD was equivalent, dermoscopy specificity was significantly better than ADD (86% compared to 78% P < 0.001) [40].…”

Section: Dermoscopymentioning

confidence: 97%

Screening and Non-Invasive Evaluative Devices for Melanoma Detection A Comparison of Commercially Available Devices and Dermoscopic Evaluation

Grossman¹

2015

CDT

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A number of commercially available devices have been developed to help dermatologists distinguish benign pigmented lesions from malignant melanoma, but it is unclear if and how these technologies should be adopted into clinical practice. This review summarizes the reported diagnostic accuracies of these devices and compares them to clinical exam and dermoscopy. Screening devices appear to have the highest utility in patient populations with a high prevalence of melanoma. However, even in high-risk patients the diagnostic accuracy of evaluative devices is not clearly superior to dermatologists skilled with dermoscopy, potentially limiting their cost effectiveness. General practitioners not skilled with dermoscopy are likely to have larger improvements in diagnostic accuracy when using evaluative devices, however further investigation into the best way to employ them is needed.

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Section: Dermoscopymentioning

confidence: 97%

Screening and Non-Invasive Evaluative Devices for Melanoma Detection A Comparison of Commercially Available Devices and Dermoscopic Evaluation

Grossman¹

2015

CDT

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“…Using meta-analytical methods Authors of [53] compared the diagnostic accuracy of the different dermoscopic algorithms with each other and with the artificial intelligence methods for the detection of melanoma. They concluded that pooled sensitivity for artificial intelligence was slightly higher than for dermoscopy (91% vs. 88%) and pooled specificity for dermoscopy was significantly better than artificial intelligence (86% vs. 79%).…”

Section: Related Workmentioning

confidence: 99%

Search for Resolution Invariant Wavelet Features of Melanoma Learned by a Limited ANN Classifier

Surówka¹

2017

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Abstract. This article addresses the Computer Aided Diagnosis (CAD) of melanoma pigmented skin cancer. We present back-propagated Artificial Neural Network (ANN) classifiers discriminating dermoscopic skin lesion images into two classes: malignant melanoma and dysplastic nevus. Features used for our classification experiments are derived from wavelet decomposition coefficients of the image. Our research objective is i) to select the most efficient topology of the hidden layers and the network learning algorithm for full-size and downgraded image resolutions and, ii) to search for resolution-invariant topologies and learning methods. The analyzed classifiers should be fit to work on ARM-based hand-held devices, hence we take into account only limited learning setups.

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“…[22] or [23] for an overview). Among the many problems such a system must be able to cope with, is the fact that often the images of skin lesions will contain hair.…”

Section: Detecting Hairs In Dermascopic Images Of Skin Lesionsmentioning

confidence: 99%

Bayesian multiscale analysis of images modeled as Gaussian Markov random fields

Thon

Rue

Skrøvseth

et al. 2012

Computational Statistics & Data Analysis

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A Bayesian multiscale technique for detection of statistically significant features in noisy images is proposed. The prior is defined as a stationary intrinsic Gaussian Markov random field on a toroidal graph, which enables efficient computation of the relevant posterior marginals. Hence the method is applicable to large images produced by modern digital cameras. The technique is demonstrated in two examples from medical imaging.

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Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma

Cited by 144 publications

References 88 publications

Screening and Non-Invasive Evaluative Devices for Melanoma Detection A Comparison of Commercially Available Devices and Dermoscopic Evaluation

Screening and Non-Invasive Evaluative Devices for Melanoma Detection A Comparison of Commercially Available Devices and Dermoscopic Evaluation

Search for Resolution Invariant Wavelet Features of Melanoma Learned by a Limited ANN Classifier

Bayesian multiscale analysis of images modeled as Gaussian Markov random fields

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