Improved Model of Eye Disease Recognition Based on VGG Model

The rapid development of computer vision technology and digital images has increased the potential for using image recognition for eye disease diagnosis. Many early screening and diagnosis methods for ocular diseases based on retinal images of the fundus have been proposed recently, but their accuracy is low. Therefore, it is important to develop and evaluate an improved VGG model for the recognition and classification of retinal fundus images. In response to these challenges, to solve the problem of accuracy and reliability of clinical algorithms in medical imaging this paper proposes an improved model for early recognition of ophthalmopathy in retinal fundus images based on the VGG training network of densely connected layers. To determine whether the accuracy and reliability of the proposed model were greater than those of previous models, our model was compared to ResNet, AlexNet, and VGG by testing them on a retinal fundus image dataset of eye diseases. The proposed model can ultimately help accelerate the diagnosis and referral of these early eye diseases, thereby facilitating early treatment and improved clinical outcomes.