Machine Learning Algorithms for Ocular Disease from Fundus Images using LBP and HOG
Keywords:
Fundus images, Histogram of Oriented Gradients, K-Nearest Neighbors, Local Binary Patterns, Ocular Disease, Random Forests.Abstract
Despite their diminutive size, eyes are essential to human life. Given the importance of the visual system among the four sense organs and the variety of eye disorders that might arise, it is imperative to identify abnormalities of the external eye as soon as possible. Severe visual impairment or blindness can result from ocular illness, a progressive eye disorder associated with diabetes. To diagnose and cure it, specialists use non-invasive images of the retina called fundus imaging. Expert knowledge and high-quality images are prerequisites for accurate picture classification. Eight distinct groups of ocular diseases that cause blindness were taken into consideration in the suggested effort. The dual-stage method for identifying ocular disorders described in this article uses the Histogram of Oriented Gradients (HOG) and local binary patterns (LBP) for feature extraction. Next, machine learning methods like support vector machines (SVM), random forests (RF), and K-Nearest Neighbours (KNN) are used for classification operations. This study contrasts eight methods for identifying eye diseases. The results show that the combination of HOG and SVM, with an accuracy of 92.2%, and LBP and SVM, with a combination of 98.1%, attained the maximum accuracy.
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