Category CS P07 Catch it Early: Web-Based Screening for Melanoma

Abstract Artificial intelligence has the potential to improve early detection rates for

melanoma. If caught early melanoma, the most dangerous form of

malignant skin cancer, can be surgically removed with a near 100 percent

survival rate. Despite this, melanoma kills more than 60,000 people every

year. Identifying early-stage melanoma with the naked eye is exceptionally

hard, meaning that the average person is unaware of their melanoma until

later stages. An automated diagnosis system was created using artificial

neural networks and principal components analysis. The system screens

images for signs of melanoma and produces a positive or negative result.

The computer-aided melanoma diagnosis system achieved an overall

accuracy of 75%. A freely available website was created using the

system. The website will allow more accurate self-examinations, second-

opinions for dermatologists, or clinical use in rural areas or third-world

countries. Hopefully, this will increase early detection of melanoma and

reduce mortality rates.

Bibliography [1] – R. Lucas; T. McMichael; W. Smith; B. Armstrong, Solar Ultraviolet

Radiation: Global burden of disease from solar ultraviolet radiation, 2006.



[2] – M.B. Lens; M. Dawes, Epidemiological Trends of Cutaneous Malignant

Melanoma, 2004.



[3] – NIH Consensus Conference: Diagnosis and treatment of early

melanoma, 1992.



[4] – Friedman RJ; Rigel DS; Kopf AW, Early detection of malignant

melanoma: the role of the physician examination and self-examination of

the skin, 1985.



[5] – B. Rosado; S. Menzies; A. Harbauer; H. Pehamberger; K. Wolff; M.

Binder; H. Kittler, Accuracy of computer aided diagnosis of melanoma: a

quantitative meta-analysis, 2003.



[6] – M. Healsmith; J. Bourke; J. Osborne; R. Graham-Brown, An

evaluation of the revised seven-point checklist for the early diagnosis of

cutaneous malignant melanoma, 1993.



[7] – N. Abbasi; H. Shaw; D. Rigel; R. Friedman; W. McCarthy; I. Osman; A.

Kopf; D. Polsky, Early Diagnosis of Cutaneous Melanoma – Revisiting the

ABCD Criteria, 2004.

[8] – A. Bono; S. Tomatis; C. Bartoli; G. Tragni; G. Radaelli; A. Maurichi; R.

Marchesini, The ABCD System of Melanoma Detection - A

Spectrophotometric Analysis of the Asymmetry, Border, Color, and

Dimension, 1999.



[9] – A. MacKenzie-Wood; G. Milton; J. Launey, Melanoma: Accuracy of

clinical diagnosis – Australasian Journal of Dermatology, 1997.



[10] – A. Doubrovsky; R. Scolyer; R. Murali; P. McKenzie; G. Watson; C.

Lee; D. McLeod; W. McCarthy; R. Uren; J. Stretch; R. Saw; J. Thompson,

Diagnostic Accuracy of Fine Needle Biopsy for Metastatic Melanoma and
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