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.
Highlights
The human eye is the most important organ among the five senses, so early detection of external eye diseases has become an important issue in medicine
Gray Level Co-occurrence Matrix (GLCM) was used to extract features that fed to Bayesian Regularization Back-Propagation Neural Network (BRBPNN) classifier to identify Corneal Arcus in [7]
Hue Min Max Diff (HMMD) is a more perceptual uniform color space. It stands for Hue (same as Hue in HSV (Hue Saturation Value) color space) Min, Max, Diff are the result of transform equations from RGB into HMMD color space
Summary
The human eye is the most important organ among the five senses, so early detection of external eye diseases has become an important issue in medicine. Pattern recognition has a wide area of applications One of these applications is in the medical field. Histogram features besides texture features using Law features are extracted These features are fed into Back Propagation for classification purposes. This paper is organized as follows: In section 2, related work is discussed.
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