Texture features neural classifier of some skin diseases

Author

Abdel Wahab, Nesreen ; Abdel Wahed, Manal ; Mohamed, Abdallah S A

Author_Institution

Dept. of Syst. & Biomed. Eng., Cairo Univ.

Volume

1

fYear

2003

fDate

30-30 Dec. 2003

Firstpage

380

Abstract

This paper presents a texture classification system for three skin diseases. In this study 162 images were involved where each disease group consisted of 54 images (34 for training and 20 for testing). The set of features were preprocessed by using principal component analysis (PCA) method for features reduction. The best results were obtained by using a back propagation neural network of 17 nodes at the input layer, 11 nodes at the hidden layer, and 3 nodes at the output layer. The network correctly succeeded to classify the input images to the mentioned groups with 70% for the first disease, 70% for the second one and 100% for the last disease

Keywords

diseases; image classification; image texture; medical image processing; principal component analysis; skin; back propagation neural network; feature reduction; principal component analysis; skin diseases; texture classification system; texture features neural classifier; Analysis of variance; Diseases; Humans; Image analysis; Image recognition; Image texture analysis; Neural networks; Principal component analysis; Remote monitoring; Skin;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2003 IEEE 46th Midwest Symposium on

Conference_Location

Cairo

ISSN

1548-3746

Print_ISBN

0-7803-8294-3

Type

conf

DOI

10.1109/MWSCAS.2003.1562298

Filename

1562298