Classification of Diabetic Retinopathy based on Hybrid Neural Network

Boral, Yash S.; Thorat, Snehal

doi:10.1109/iccmc51019.2021.9418224

Cited by 20 publications

(4 citation statements)

References 16 publications

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“…Networks that have been taught to identify the outliers employed by human experts in the grading process might be used to solve this issue. Many retinal abnormalities are very tiny or have characteristics that make them difficult to diagnose [20,21] appropriately. Not only is it difficult to collect enough photos to train algorithms capable of this task, but manually annotating each important pixel in those images is both labour-intensive and time-consuming [22].…”

Section: Challenges In DL Techniquesmentioning

confidence: 99%

Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

Jagadesh,

Karthik,

Siri

et al. 2023

IEEE Access

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Diabetic Retinopathy (DR) evaluations are increasingly being automated using artificial intelligence. Diabetes-related retinal vascular disease is a major cause of blindness and visual impairment worldwide. Therefore, automated DR detection devices would greatly aid in reducing visual impairment due to DR through early screening and treatment. Researchers have provided many techniques for picking out abnormalities in retinal images during the past several years. In the past, automated methods for diagnosing diabetic retinopathy required a human to extract information from retinal images before passing them on to a classifier. This study takes a novel two-pronged approach to automated DR classification to solve the issues. Due to the low positive instance percentage of existing asymmetric, we segment O.D.s and B.V.s with an enhanced version of an improved contoured convolutional transformer (IC2T). We develop a contoured optical disc (OD), a blood vessels (BV) detection module, and a dual convolutional transformer block that combines local and global contexts to make trustworthy associations. A second-stage Improved Coordination Attention Mechanism (ICAM) network is trained to recognize retinal biomarkers for DR such as microaneurysms (M.A.), haemorrhages (H.M.), and exudates (EX). With an average accuracy of 96%, 97%, and 98% on EyePACS-1, Messidor-2, and DIARETDB0, respectively, the suggested technique has proven itself to be at the field's cutting edge. Comprehensive testing and comparisons to established methods support the proposed strategy.

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Section: Challenges In DL Techniquesmentioning

confidence: 99%

Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

Jagadesh,

Karthik,

Siri

et al. 2023

IEEE Access

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“…They further concluded that other features such as univariate, multivariate, etc., can be used for testing the proposed algorithm. Another proposed algorithm [ 10 ] used inception-v3 for the feature extraction by adaptive learning the customized different classification functions of inception-v3. They used 90 retinal images for training the framework followed by testing using 48 retinal images.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image Dataset

et al. 2022

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Diabetic Retinopathy affects one-third of all diabetic patients and may cause vision impairment. It has four stages of progression, i.e., mild non-proliferative, moderate non-proliferative, severe non-proliferative and proliferative Diabetic Retinopathy. The disease has no noticeable symptoms at early stages and may lead to chronic destruction, thus causing permanent blindness if not detected at an early stage. The proposed research provides deep learning frameworks for autonomous detection of Diabetic Retinopathy at an early stage using fundus images. The first framework consists of cascaded neural networks, spanned in three layers where each layer classifies data into two classes, one is the desired stage and the other output is passed to another classifier until the input image is classified as one of the stages. The second framework takes normalized, HSV and RGB fundus images as input to three Convolutional Neural Networks, and the resultant probabilistic vectors are averaged together to obtain the final output of the input image. Third framework used the Long Short Term Memory Module in CNN to emphasize the network in remembering information over a long time span. Proposed frameworks were tested and compared on the large-scale Kaggle fundus image dataset EYEPAC. The evaluations have shown that the second framework outperformed others and achieved an accuracy of 78.06% and 83.78% without and with augmentation, respectively.

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“…Y. S. Boral and S. S. Thorat [6] In order to increase the efficiency, this research suggests an algorithm that combines an enhanced DR detection technique. For DR feature extraction, a deep neural network technique known as Inception v3 is employed.…”

Section: Introductionmentioning

confidence: 99%

Diabetic Retinopathy Detection from Retinal Images

Zanjad

2024

IJSREM

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Diabetic Retinopathy (DR) is an eye disease that can occur in people with diabetes causing permanent or temporary blindness, where the blood vessels of retina, a layer in rear interior eye and so called light sensitive part effected by high sugar damages nerves. As I mentioned above, DR can present in various ways; Some people with sick retinas experience blurred vision, while others have difficulty seeing colors or eye floaters. At first, DR might cause no symptoms or only mild vision problems. But it can lead to blindness if left undiagnosed and untreated. This study presents a pipeline for processing and analyzing retinal images. The pipeline consists of three primary steps: (i) pre-processing images, (ii) extracting features, and (iii) classifying the results. The initial step involves pre-processing the image using various modifications to improve its quality and standardize it. Gaussian filtering has been demonstrated to work fairly well in this situation for improving the contrast of the photos. Convolutional Neural Network (CNN) based pre-trained machine learning algorithm is used to speed up the diagnosis of the severity of DR using retinal images of patients. Convolution neural networks (CNNs), among the greatest neural network architectures for image processing applications, have been utilized in the second and third stages. Keywords-Gaussian filtering,Convolutional Neural Network,Retinalimages,Diabetic retinopathy,hemorrhage,, and exudates.

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Classification of Diabetic Retinopathy based on Hybrid Neural Network

Cited by 20 publications

References 16 publications

Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image Dataset

Diabetic Retinopathy Detection from Retinal Images

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