RESEARCH ARTICLE


Diagnosis of Esophagitis Based on Face Recognition Techniques



Santosh S Saraf *, 1, Gururaj R Udupi2, Santosh D Hajare3
1 Research Center, Department of Electronics and Communications Engg., Gogte Institute of Technology, Belgaum, India
2 Vishwanathrao Deshpande Rural Institute of Technology, Haliyal, India
3 Department of Gastroenterology, K.L.E. Prabhakar Kore Hospital and Research Center, Belgaum, India


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© Saraf et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Research Center, Department of Electronics and Communications Engg., Gogte Institute of Technology, Belgaum, India; Tel: +91-831-2405500; Fax: +91-831-2441909; E-mail: santoshsaraf@git.edu


Abstract

Face recognition technology has evolved over years with the Principal Component Analysis (PCA) method being the benchmark for recognition efficiency. The face recognition techniques take care of variation of illumination, pose and other features of the face in the image. We envisage an application of these face recognition techniques for classification of medical images. The motivating factor being, given a condition of an organ it is represented by some typical features. In this paper we report the use of the face recognition techniques to classify the type of Esophagitis, a condition of inflammation of the esophagus. The image of the esophagus is captured in the process of endoscopy. We test PCA, Fisher Face method and Independent Component Analysis techniques to classify the images of the esophagus. Esophagitis is classified into four categories. The results of classification for each method are reported and the results are compared.

Keywords: Decision support system, medical diagnosis, principal component analysis.