scholarly journals Analysis of Chronic Skin Diseases using Artificial Neural Network

2018 ◽  
Vol 179 (31) ◽  
pp. 7-13 ◽  
Author(s):  
Sudhakar Singh ◽  
Shabana Urooj
Author(s):  
Revati Kadu ◽  
U. A. Belorkar

One of the most common and augmenting health problems in the world are related to skin. The most  unpredictable and one of the most difficult entities to automatically detect and evaluate is the human skin disease because of complexities of texture, tone, presence of hair and other distinctive features. Many cases of skin diseases in the world have triggered a need to develop an effective automated screening method for detection and diagnosis of the area of disease. Therefore the objective of this work is to develop a new technique for automated detection and analysis of the skin disease images based on color and texture information for skin disease screening. In this paper, system is proposed which detects the skin diseases using Wavelet Techniques and Artificial Neural Network. This paper presents a wavelet-based texture analysis method for classification of five types of skin diseases. The method applies tree-structured wavelet transform on different color channels of red, green and blue dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. In all 99 unique features are extracted from the image. By using Artificial Neural Network, the system successfully detects different types of dermatological skin diseases. It consists of mainly three phases image processing, training phase, detection  and classification phase.


Author(s):  
Revati Kadu ◽  
U. A. Belorkar

One of the most common and augmenting health problems in the world are related to skin. The most  unpredictable and one of the most difficult entities to automatically detect and evaluate is the human skin disease because of complexities of texture, tone, presence of hair and other distinctive features. Many cases of skin diseases in the world have triggered a need to develop an effective automated screening method for detection and diagnosis of the area of disease. Therefore the objective of this work is to develop a new technique for automated detection and analysis of the skin disease images based on color and texture information for skin disease screening. In this paper, system is proposed which detects the skin diseases using Wavelet Techniques and Artificial Neural Network. This paper presents a wavelet-based texture analysis method for classification of five types of skin diseases. The method applies tree-structured wavelet transform on different color channels of red, green and blue dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. In all 99 unique features are extracted from the image. By using Artificial Neural Network, the system successfully detects different  types of dermatological skin diseases. It consists of mainly three phases image processing, training phase, detection  and classification phase.


2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  

1998 ◽  
Vol 14 (3) ◽  
pp. 226-233 ◽  
Author(s):  
Jürgen Hoyer ◽  
Mechthild Averbeck ◽  
Thomas Heidenreich ◽  
Ulrich Stangier ◽  
Karin Pöhlmann ◽  
...  

Epstein's “Constructive Thinking Inventory” (CTI) was developed to measure the construct of experiential intelligence, which is based on his cognitive-experiential self-theory. Inventory items were generated by sampling naturally occurring automatic cognitions. Using principal component analysis, the findings showed a global factor of coping ability as well as six main factors: Emotional Coping, Behavioral Coping, Categorical Thinking, Personal Superstitious Thinking, Esoteric Thinking, and Naive Optimism. We tested the replicability of this factor structure and the amount of statistical independence (nonredundancy) between these factors in an initial study of German students (Study 1, N = 439) and in a second study of patients with chronic skin disorders (Study 2, N = 187). Factor congruence with the original (American) data was determined using a formula proposed by Schneewind and Cattell (1970) . Our findings show satisfactory factor congruence and statistical independence for Emotional Coping and Esoteric Thinking in both studies, while full replicability or independence could not be found in both for the other factors. Implications for the use and further development of the CTI are discussed.


1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman

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