Abstract
The feature extraction stage remains a major component of every biometric recognition system. In most instances, the eventual accuracy of a recognition system is dependent on the features extracted from the biometric trait and the feature extraction technique adopted. The widely adopted technique employs features extracted from healthy retinal images in training retina recognition system. However, literature has shown that certain eye diseases such as diabetic retinopathy (DR), hypertensive retinopathy, glaucoma, and cataract could alter the recognition accuracy of the retina recognition system. This connotes that a robust retina recognition system should be designed to accommodate healthy and diseased retinal images. A framework with two different approaches for retina image recognition is presented in this study. The first approach employed structural features for healthy retinal image recognition while the second employed vascular and lesion-based features for DR retinal image recognition. Any input retinal image was first examined for the presence of DR symptoms before the appropriate feature extraction technique was adopted. Recognition rates of 100% and 97.23% were achieved for the healthy and DR retinal images, respectively, and a false acceptance rate of 0.0444 and a false rejection rate of 0.0133 were also achieved.
Highlights
In this era of cutting-edge technology, the demand for a reliable security system is increasing like that of biometric security systems, which employ unique human physical, chemical, and behavioral traits in identifying and authenticating the user of a biometric system
The retina infected by diabetic retinopathy (DR) shows signs of lesions such as microaneurysms, cotton wool spots, exudates, macular edema, and hemorrhages [14]. All these symptoms of DR as seen in retinal images could have adverse effects on the recognition accuracy of retina recognition systems [6, 15, 16]. erefore, researchers have identified the feature extraction stage as an important stage that could improve the recognition accuracy of biometric recognition systems [1, 3, 17, 18]. ough existing works have proposed several novel feature extraction techniques for healthy or unhealthy retinal images, this study presents a framework that could accommodate both healthy and DR retinal images
Literature has revealed that certain eye diseases such as glaucoma, hypertensive retinopathy, diabetes retinopathy, and cataract could alter or eventually damage the patterns of retina blood vessels. erefore, this study proposed a technique to extract features from both healthy and diabetic retinopathy retinal images for the purpose of human recognition
Summary
Implementation of a Framework for Healthy and Diabetic Retinopathy Retinal Image Recognition. E feature extraction stage remains a major component of every biometric recognition system. The eventual accuracy of a recognition system is dependent on the features extracted from the biometric trait and the feature extraction technique adopted. E widely adopted technique employs features extracted from healthy retinal images in training retina recognition system. Is connotes that a robust retina recognition system should be designed to accommodate healthy and diseased retinal images. A framework with two different approaches for retina image recognition is presented in this study. Any input retinal image was first examined for the presence of DR symptoms before the appropriate feature extraction technique was adopted. Recognition rates of 100% and 97.23% were achieved for the healthy and DR retinal images, respectively, and a false acceptance rate of 0.0444 and a false rejection rate of 0.0133 were achieved
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