Multiple Eye Disease Detection using HOG and LBP on Convolution Neural Network
Keywords:
Cataract, Convolution Neural Network, multiple eye disease, Optic, Optical Coherence Tomography.Abstract
The eyes are among the most crucial systems in the human body. Despite their small size, life without vision is unimaginable for humans. The eyes are protected from dust particles by a thin layer known as the conjunctiva, which acts as a lubricant and reduces friction during the opening and closing of the eyes. A cataract refers to the clouding of the eye's lens. Multiple eye disease exists, and since the visual system is the most vital of the four sensory organs, it is essential to detect external eye abnormalities early. In the proposed method HOG and LBP approach are used for feature extraction. Further, the research involves using Convolution Neural Networks (CNN) to analyze Optical Coherence Tomography (OCT) scans for detecting multiple eye diseases. In this study, a CNN was applied to OCT images from a validated dataset, achieving an accuracy of 91% through 5-fold cross-validation. The highest Area Under the Curve (AUC) value observed for the normal class was 1. The proposed method achieved over 90% AUC in predicting eye diseases across all classes.
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