Automated Diagnosis of Skin Diseases Using Gray Level Co-occurrence Matrix with Back-Propagation Neural Network Algorithm

Authors

  • Golam Kibria Department of Computer Science & Engineering Rajshahi University of Engineering & Technology, Bangladesh
  • Md. Rabiul Islam Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology

Abstract

Various aspects of skin diseases inspired us to develop an automated diagnosis system of the disease and its affecting area(s).  The objective of this work is to analyze the methods to auto-detect skin diseases and to find out an optimal approach taking both diseases’ nature and their variety in consideration. Gray Level Co-occurrence Matrix (GLCM) defines the probability of occurrence of a gray level within its neighbors at a specific distance. Five GLCM features (Contrast, Homogeneity, Dissimilarity, Correlation and Entropy) have been extracted from the source images. A feed-forward neural network with Back Propagation Neural Network (BPNN) algorithm as the learning method is chosen. Mean Square Error (MSE) is selected as the error calculation method. Six skin disease types (Acne, Eczema, Erythema, Ichthyosis, Psoriasis and Scabies) have been classified. The average accuracy of the reference method is 79%, and it has been improved to 83.3%.

Keywords: Gray Level Co-occurrence Matrix (GLCM), medical imaging, skin diseases screening, Back Propagation Neural Network (BPNN)

Cite this Article

Kibria G, Islam MR. Automated Diagnosis of Skin Diseases Using Gray Level Co-occurrence Matrix with Back-Propagation Neural Network Algorithm. Research and Reviews: Journal of Computational Biology (RRJoCB). 2015; 4(1): 22–27p.


 

Downloads

Published

2015-04-22

Issue

Vol. 4 No. 1 (2015)

Section

Research/Review Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is notpublished already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  • I/We also agree to the authorship of the article in the following order:

Author’s name

 

1. ________________

2. ________________

3. ________________

_______________

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.