Paper
9 March 1999 Application of GHA neural network to the characterization of skin tumors
Camille Serruys, Djamel Brahmi, Alain Giron, Joseph Vilain, Raoul Triller, Bernard Fertil
Author Affiliations +
Abstract
The prognosis of malignant melanoma strongly relies on tumor early detection. Unfortunately, differentiating early melanomas from other less dangerous pigmented lesions is a difficult task since they have near physical characteristics. Dermatoscopy is a new non-invasive technique, which, by oil immersion, makes subsurface structures of skin accessible to in vivo examination. Our objective is to develop a computer diagnosis system applied to dermatoscopic images of skin tumors. Most of the signs for the visual diagnosis of melanoma only require the examination of part of the tumors. Our approach consists in classifying windows taken from images of skin tumors by a two-stage procedure. First, a Generalized-Hebbian-Algorithm- based network operates a Principal Component-like Analysis of windows. Sets of primitive windows fitted to various contexts allow both contextual coding and compression of windows. The second stage involves a classical feedforward network, which performs the classification of windows on the basis of the contribution of each primitive window to the reconstruction of windows under consideration. It was shown that classification was properly achieved when 20 primitive windows at least were considered. Application to the classification of skin tumors is in progress and preliminary results dealing with the characterization of borders of lesions are presented.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Camille Serruys, Djamel Brahmi, Alain Giron, Joseph Vilain, Raoul Triller, and Bernard Fertil "Application of GHA neural network to the characterization of skin tumors", Proc. SPIE 3647, Applications of Artificial Neural Networks in Image Processing IV, (9 March 1999); https://doi.org/10.1117/12.341122
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KEYWORDS
Tumors

Skin

Melanoma

Image classification

Computing systems

Spatial frequencies

Neural networks

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