Eye Diseases Classification Using Back Propagation Artificial Neural Network

Authors

• Hanaa M. Ahmed, Department of Computer Science, University of Technology, Baghdad, Iraq, 110113@uotechnology.edu.iq.
• Shrooq R. Hameed, Department of Computer Science, University of Technology, Baghdad, Iraq, shrooq_rasheed@yahoo.com.

Keywords

Back Propagation, Eye Diagnosis, feature extraction, Hue, Min, Max, Diff (HMMD) color space, Laws Feature

Document Type

Research Paper

Abstract

A human eye is a vital organ responsible for a person's vision. So, the early detection of eye diseases is essential. The objective of this paper deals with diagnosing of seven different external eye diseases that can be recognized by a human eye. These diseases cause problems either in eye pupil, in sclera of eye or in both or in eyelid. Color histogram and texture features extraction techniques with classification technique are used to achieve the goal of diagnosing external eye diseases. Hue Min Max Diff (HMMD) color space is used to extract color histogram and texture features which were fed to Back Propagation Artificial Neural Network (BPANN) for classification. The comparative study states that the features extracted from HMMD color space is better than other features like Histogram of Oriented Gradient (HOG) features and give the same accuracy as features extracted directly from medical expert recorded symptoms. The proposed method is applied on external eye diseases data set consisting of 416 images with an accuracy rate of 85.26315%, which is the major result that was achieved in this study.

Recommended Citation

M. Ahmed, Hanaa and R. Hameed, Shrooq () "Eye Diseases Classification Using Back Propagation Artificial Neural Network," Engineering and Technology Journal: Vol. 39: Iss. 1, Article 17.
DOI: https://doi.org/10.30684/etj.v39i1B.1363

DOI

10.30684/etj.v39i1B.1363

First Page

11

Last Page

20