Eye Diseases Classification Using Back Propagation Artificial Neural Network

M. Ahmed, Hanaa; R. Hameed, Shrooq

doi:10.30684/etj.v39i1B.1363

	Eye Diseases Classification Using Back Propagation Artificial Neural Network
Engineering and Technology Journal
Article 2, Volume 39, 1B, 2021, Pages 11-20 PDF (624.8 K)
DOI: 10.30684/etj.v39i1B.1363
Authors
Hanaa M. Ahmed¹; Shrooq R. Hameed^* ²
¹Department of Computer Science, University of Technology, Baghdad, Iraq, [email protected].
²Department of Computer Science, University of Technology, Baghdad, Iraq, [email protected].
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.
Keywords
Back Propagation; Eye Diagnosis; feature extraction; Hue; Min; Max; Diff (HMMD) color space; Laws Feature

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