Limitations of Dermoscopy in the Recognition of Melanoma

Early detection of malignant melanoma remains the key factor in lowering mortality from this cancer. Recognizing the importance of this issue 25 years ago, our group at New York University published in CA: A Cancer Journal for Clinicians the mnemonic "ABCD" to facilitate the early diagnosis of melanoma. Studies have demonstrated the usefulness of this paradigm in enhancing early melanoma diagnosis as a part of clinical examinations, mass screenings, and public education programs. Approaches to melanoma diagnosis have dynamically evolved during the ensuing quarter century. In the 1990s, dermoscopy enabled the recognition of new subsurface features to differentiate between malignant and benign pigmented lesions. During the last decade, new computer-based technologies have improved diagnostic sensitivity and specificity and may result in optimizing lesion selection for biopsy and pathology review. Despite all of the advances in melanoma diagnosis, timely recognition, detection, and rapid treatment of melanoma remain critical. Although pathologic examination remains the gold standard for diagnosis, this cancer has the potential to be diagnosed through noninvasive approaches because of its cutaneous location. From the development of the ABCDs through current attempts that use complex computer algorithms and genetic markers, a clinician's ability to detect melanoma in its earliest form has been augmented. However, a "good clinical eye" is still fundamental to selecting the lesions for evaluation among the sea of those that are prevalent. As current approaches are refined and new techniques are developed, the improved ability to diagnose this cancer will hopefully enhance reaching the goal of reducing melanoma mortality. CA Cancer J Clin 2010;60:301-316.

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“…The downside of dermoscopy is its potential failure to detect very early or "featureless" (amelanotic) melanomas. 73 …”

Section: Using Technology To Augment Early Melanoma Diagnosis Dermoscopymentioning

confidence: 99%

The Evolution of Melanoma Diagnosis: 25 Years Beyond the ABCDs

Rigel

Russak

Friedman

2010

CA: A Cancer Journal for Clinicians

336

274

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“…The standard therapy, biopsy, is not feasible for all the patients due to treatment costs and some health reasons. Even if affordable, excision must be done at an early stage, when standard clinical diagnosis based on the ABCD criteria may fail [4]. For that reason, analysis of skin lesions images have become a useful diagnostic tool.…”

Section: Introductionmentioning

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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“…Devel opment of appropriate diagnostic techniques for measuring and monitoring skin abnormalities is currently one of the challenging areas of research [3][4][5]. Most of diagnostic methods and evaluation methods for measuring the progress of skin disease are based on visual examination by dermatologists [14].…”

Section: Introductionmentioning

confidence: 99%