Skin Cancer recognition by using Neuro-Fuzzy system.
Skin cancer is a major public health problem. It is the most prevalent cancer in the light-skinned population and is generally caused by exposure to ultraviolet light. Early detection of skin cancer
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has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose skin cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the clinician. To obviate these problems, image processing techniques, a neural network system (NN) and a fuzzy inference system were used in this study as promising modalities for detection of different types of skin cancer.The single most promising strategy to cut acutely the mortality rate from melanoma is early detection. Attempts to improve the diagnostic accuracy of melanoma have spurred the development of innovative in-vivo imaging modalities, including total body photography, dermoscopy, automated diagnostic system and reflectance confocal microscopy. Neural networks (NN) are a large class of models developed in the cognitive sciences, the structure of which was inspired by that of the nervous system of living beings.
This research proposes a system to make the computer automatically locate the tumour location in the image and calculate relevant features and such features can be used to determine the type of skin cancer.
The accuracy rate of the diagnosis of skin cancer by using the hierarchal neural network was 90.67% while using neuro-fuzzy system yielded a slightly higher rate of accuracy of 91.26% in diagnosis skin cancer type (Fuzzy logic is a form of multi-valued logic that deals with reasoning that is approximate rather than precise.)
Authors: Bareqa Salah, Mohammad Alshraideh, Rasha Beidas and Ferial Hayajneh.
Source: Libertas Academia http://www.la-press.com. doi: 10.4137/CIN.S5950 "Cancer Informatics”
Publication date: 2nd Feb, 2011
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