Abstract:
Cataract, a common eye disorder characterized by clouding of the lens, is a leading cause of vision
loss. Each year numerous numbers of people are falling sufferer to visionary loss globally. The
majority of the time, this problem arises as people age. This problem also can occur in young age
people because of injury or certain clinical situations. It's far known as cataract while a dense and
cloudy layer create on the eye lens and consequences the clear vision which can cause a problem
like blurry eyesight, diminished vision and prescient. Additionally, they face difficulty seeing in
robust light and gradually it could be the reason for full blindness. An excellent way to manipulate
the hazard and avoid blindness is to stumble on cataracts well-timed and correctly before it
become more complicated. In this study, we propose a cataract detection system using deep
learning and image processing techniques. Our system aims to automatically analyze ocular
images and predict the presence of cataracts with high accuracy. We are trying to pick out an
efficient and accurate manner of detecting cataracts primarily based on a Deep Convolutional
Neural network (DCNN) with the publicly accessed dataset. We used the transfer learning
methods with DCNN models which are VGG19, NASnet, Resnet50 and MobileNetV2 achieving
the highest accuracy across 2000 image sets. Also, MobilNnetV2 achieved accuracy rates of
97.75% on the test images. Compared to other models, the final result indicates that MobileNetV2
takes the least time to recognize images and classify them.
