Deep Learning in Eye Image Analysis and Retinal Disease Computer Image Visual Recognition System

Abstract

Image analysis technology based on deep learning provides strong support for the early diagnosis and treatment of retinal diseases in diabetic patients. This paper focuses on a multi-feature fusion based convolutional neural network (CNN) recognition algorithm, which combines OpenCV image processing tools to realize efficient and accurate retinopathy image recognition of diabetic patients. The model extracts the deep features of retinal images by deep convolutional network and integrates the image information of different levels effectively by using multi-scale feature fusion strategy. The model further introduced the attention mechanism to enhance the attention to the lesion area, thus improving the accuracy of the lesion detection. In addition, the application of OpenCV tool greatly simplifies the retinal image preprocessing process of diabetic patients, ensuring the normalization and consistency of input data. In order to verify the effectiveness of the algorithm, a lot of simulation experiments are carried out in this paper. The experimental results show that compared with traditional methods, the proposed algorithm has higher accuracy and faster processing speed in the retinopathy image recognition task of diabetic patients. Especially in the complex background of diabetic retinopathy detection, the algorithm has shown significant advantages.