The Performance of SolarScan

Menzies, Scott W.; Bischof, Leanne; Talbot, Hugues; Gutenev, Alex; Avramidis, Michelle; Wong, Livian; Lo, Sing Kai; Mackellar, Geoffrey; Skladnev, Victor; McCarthy, William H.; Kelly, John W.; Cranney, Brad; Lye, Peter; Rabinovitz, Harold; Oliviero, Margaret; Blum, Andreas; Virol, Alexandra; De’Ambrosis, Brian; McCleod, Roderick; Koga, Hiroshi; Grin, Caron M.; Braun, Ralph P.; Johr, Robert H.

doi:10.1001/archderm.141.11.1388

Cited by 130 publications

(36 citation statements)

References 22 publications

(8 reference statements)

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“…However, dermoscopy [8] is a noninvasive diagnostic technique that links clinical dermatology and dermatopathology by enabling the visualization of morphological features which are not discernible by examination with the naked eye. There are different techniques, like solar scan [9], epiluminescence microscopy (ELM) [10, 11], cross-polarization epiluminescence (XLM), and side transillumination (TLM) [12, 13], that can greatly increase the morphological details that are visualized. Thus, they provide additional diagnostic criteria to the dermatologist.…”

Section: Introductionmentioning

confidence: 99%

“…However, it has also been demonstrated that dermoscopy may actually lower the diagnostic accuracy in the hands of inexperienced dermatologists [10, 18–20], since this method requires great deal of experience to differentiate skin lesions [21]. As described in [9, 22] only experts have arrived at 90% sensitivity and 59% specificity in skin lesion diagnosis, while for less trained doctors these figures show significant drop till around 62%-63% for general practitioners.…”

Section: Introductionmentioning

confidence: 99%

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Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

283

161

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Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

283

161

View full text Add to dashboard Cite

show abstract

“…40-45 Some have achieved a degree of sensitivity and specificity approaching expert dermoscopists. 37,43,45-48 On the basis of these encouraging results, scientists are working to develop fully automated diagnosis systems. 38,43,49 …”

Section: Computer-assisted Diagnosis Of Pigmented Lesionsmentioning

confidence: 99%

Current and emerging technologies in melanoma diagnosis: the state of the art

Psaty

Halpern

2009

Clinics in Dermatology

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Relative to other specialties, dermatologists have been slow to adopt advanced technologic diagnostic aids. After all, most skin disease can be diagnosed by simple visual inspection, and the skin is readily accessible for a diagnostic biopsy. Diagnostic aids such as total body photography and dermoscopy improve the clinician's ability to diagnose melanoma beyond unaided visual inspection, however, and are now considered mainstream methods for early detection. Emerging technologies such as in vivo reflectance confocal microscopy are currently being investigated to determine their utility for noninvasive diagnosis of melanoma. This review summarizes the currently available cutaneous imaging devices and new frontiers in noninvasive diagnosis of skin disease. We anticipate that multimodal systems that combine different imaging technologies will further improve our ability to detect, at the bedside, melanoma at an earlier stage.

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“…This study may be reported for the first time in the international, which will lay foundations for solving the key problem of intellectualized recognition for CMM in the yellow race [7,8] .…”

Section: Introductionmentioning

confidence: 99%

Early diagnosis for cutaneous malignant melanoma based on the intellectualized classification and recognition for images of melanocytic tumour by dermoscopy

Meng

Xiao²,

Xie³

et al. 2012

JBGC

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Background: In recent years, the morbidity of Melanocytic Tumor, especially Cutaneous Malignant Melanoma, has been increasing year by year. Cutaneous Malignant Melanoma has been the most fatal skin disease owing to its high malignant level and proneness to metastasis. It is of great importance to establish the intellectualized classification and recognition for Melanocytic Tumor in the yellow race, in order to realize the early diagnosis and reduce the mortality for Cutaneous Malignant Melanoma. Methods:We adopted the polarized-light dermoscopy image technology; acquired the images of Melanocytic Tumor from the yellow race in a non-invasive way; proposed the algorithms of adaptive clustering segmentation and feature extraction; quantificationally analyzed the six features of Melanocytic Tumor images, including its asymmetry, eccentricity, border depressed rate, uniformity of radiation in transition area, color diversity and texture correlation; realized the classification and intellectualized recognition for benign or malignant Melanocytic Tumor, with the combined neural network classifier; verified the results by histopathology and statistical analysis.Results: Among the 642 images, the benign ones accounted for 82.4%, the malignant ones 17.6%. The sensitivity and the specificity of uniformity of radiation in transition area, texture correlation, border depressed rate and color diversity were from 86.73% to 95.58%, and from 97.3% to 100% respectively, which showed relatively high performance. The sensitivity and the specificity of asymmetry and eccentricity were from 41.59% to 47.78%, and from 69.91% to 76.99% respectively, which showed relatively low performance. The average classification accuracy for benign and malignant Melanocytic Tumor reached 93.65% by the multiple independent neural network classifiers, the difference of which was significant by chi-square test (χ 2 = 4.51, P＜0.05). Conclusions:The polarized-light dermoscopy analysis technology is able to realize the feature classification and automatic recognition for benign or malignant Melanocytic Tumor in a non-invasive way. It will lay foundations for solving the key problem of early diagnosis and intellectualized recognition for Cutaneous Malignant Melanoma in the yellow race.

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The Performance of SolarScan

Cited by 130 publications

References 22 publications

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Current and emerging technologies in melanoma diagnosis: the state of the art

Early diagnosis for cutaneous malignant melanoma based on the intellectualized classification and recognition for images of melanocytic tumour by dermoscopy

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