Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network

Abstract

Diabetic retinopathy is characteristic of a local distribution that involves early-stage risk factors and can forecast the evolution of the illness or morphological lesions related to the abnormality of retinal blood flows. Regional variations in retinal blood flow and modulation of retinal capillary width in the macular area and the retinal environment are also linked to the course of diabetic retinopathy. Despite the fact that diabetic retinopathy is frequent nowadays, it is hard to avoid. An ophthalmologist generally determines the seriousness of the retinopathy of the eye by directly examining color photos and evaluating them by visually inspecting the fundus. It is an expensive process because of the vast number of diabetic patients around the globe. We used the IDRiD data set that contains both typical diabetic retinopathic lesions and normal retinal structures. We provided a CNN architecture for the detection of the target region of 80 patients’ fundus imagery. Results demonstrate that the approach described here can nearly detect 83.84% of target locations. This result can potentially be utilized to monitor and regulate patients.

Comparison of Proposed DCNN Model With Standard CNN Architectures for Retinal Diseases Classification

“…The MIDNET18 model is evaluated using OCT retinal images, and the results were compared with existing standard models like ResNet50, DenseNet. According to Ranjbarzadeh et al (2021), 19 the sample size for the study was 14 with the parameters alpha 0.05, beta 0.2, and g-power 0.8, as indicated in Figure 3. Four study groups are considered for this study.…”

DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

“…In today's pattern recognition strategies and their applications in many fields, the convolutional neural network (CNN) structures illustrate a massive breakthrough in data analyzing and processing. The CNN models principally deduce the relation between some key details, textural content and are utilized at the core of every model from data mining to the prediction of visiting new sites by people 32,33 …”

“…The CNN models principally deduce the relation between some key details, textural content and are utilized at the core of every model from data mining to the prediction of visiting new sites by people. 32,33 Just like Artificial Neural Networks (ANNs), the CNN structures are based on neurons and have a grid-like topology. These models qualify us to exploit key information and characteristics from the POIs and friendships efficiently using a series of convolution layers with the user-defined size of kernels.…”

Recognizing the Damaged Surface Parts of Cars in the Real Scene Using a Deep Learning Framework

“…e employed 3 × 3mReLU architecture is shown in detail in Figure 2. is model was inspired by the concept of learning multiple patterns using intermediate layers in a CNN model [25,28,29]. In other words, utilizing stacking up the mReLU pipeline is more efficient than a simple linear chain of convolution layers to classify a varying-scale object.…”