Using a Deep Learning System That Classifies Hypertensive Retinopathy Based on the Fundus Images of Patients of Wide Age

Abstract

Range throughout Turkey in this paper, the author trained the continuous neural networks, and used a total of 4,000 fundus images, including images with different degrees of fundus disorders and images without disorders, so that CNN can detect whether the patient has hypertension and arteriosclerosis according to macular degeneration in the fundus images. In order to obtain more effective results from the deep learning structure using convolutional neural network, this paper prepared more data sets on the basis of Turkey, combined with the local data sets to educate the deep learning model, so as to integrate the data globally, which can help standardize the results and improve the accuracy. The system is used to diagnose retinal vascular degeneration, such as fundus vascular disease and macular edema disease. Based on this basic understanding, the research has been used for the detection and classification of hypertensive retinopathy that has similar causes. The author also points out the limitations of the system. Among them, the most important limitation is the need for long-term financial sustainability.

A Systematic Study of Deep Learning Architectures for Analysis of Glaucoma and Hypertensive Retinopathy

“…In order to classify the features and emphasize the severity, such as reading the diabetic retinopathy and AVR, a new model using CNN architecture based on DenseNet was proposed. A comparative study of the performance of deep learning algorithms versus traditional approaches in limited data sources for detecting Hypertensive Angiopathic Retinopathy was conducted by researchers in [23], The study was carried out in a clinical environment based on a multi-racial population.…”

Embedded Implementation of Social Distancing Detector Based on One Stage Convolutional Neural Network Detector

“…Recently, the Corona Virus Disease 2019 (COVID- 19) is affecting 215 countries and territories around the world. COVID-19 has infected more than 42 million people and killed more than one million persons [1].…”

“…CNN was successfully used to solve many daily live tasks such as indoor navigation [10,11], scene recognition [12], indoor object recognition [13], traffic signs classification and detection [14,15], pedestrian detection [16,17] and many other applications [18,19]. The main problem of CNN that is computationally expensive and need high-performance computers to achieve the desired performance.…”