Abstract:
Among the most important systems in the body is the eyes. Although their
small stature, humans are unable to imagine existence without it. The human
optic is safe against dust particles by a narrow layer called the conjunctiva. It
prevents friction during the opening and shutting of the eye by acting as a
lubricant. A cataract is an opacification of the eye's lens. There are various
forms of eye problems. Because the visual system is the most important of the
four sensory organs, external eye abnormalities must be detected early. The
classification technique can be used in a variety of situations. A few of these
uses are in the healthcare profession. We use visual geometry group (VGG16), ResNet-50, and Inception-v3 architectures of convolutional neural
networks (CNNs) to distinguish between normal eyes, conjunctivitis eyes, and
cataract eyes throughout this paper. With a detection time of 485 seconds,
Inception-v3 is the most accurate at detecting eye disease, with a 97.08%
accuracy, ResNet-50 performs the second-highest accuracy with 95.68% with
1090 seconds and lastly, VGG-16 performs 95.48% accuracy taking the
highest time of 2510 seconds to detect eye diseases.