WITHDRAWN: Diabetic retinopathy detection using convolutional nueral networks algorithm

Sridhar, S.; Pradeepkandhasamy, J.; Sinthuja, M.; Minish, T.N. Sterlin

doi:10.1016/j.matpr.2021.01.059

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…Various standard metrics like that as f1 measure, recall, accuracy and precision were computed, as well as segmentation measures such as DSC, JSC, and time period. The performance of six different classifiers, SVM-GSO [26], PSO-CNN [27], CNN [28], DCNN-EMFO [29], MACO-CNN [30], and the suggested IGWO-FFOCNN, is shown in the figure9. It shows that the suggested IGWO-FFOCNN has a higher TNR outcome percentage and a lower FPR, FNR.…”

Section: Resultsmentioning

confidence: 99%

improved grey wolf optimization algorithm (iGWO) for the detection of diabetic retinopathy using convnets and region based segmentation techniques

Vijayalakshmi¹,

Kumar²

2022

ijhs

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Diabetic Retinopathy is a retinal eye disease that affects people with Diabetes Mellitus. DM is metabolic disorder and it is caused by the high glucose level in blood. This leads to eye deficiency called Diabetic Retinopathy (DR). In this paper, we proposed iGWO algorithm for the detection and diagnosis of DR stages and Region growing technique is used for segmentation of the images. Various preprocessing techniques are followed for the better enhancement of the images. An improved Grey Wolf Optimization (iGWO) algorithm are proposed to obtain the global optimum. Convnets are utilized for DR categorization. The iGWO-FFOCNN model is compared with existing technologies like SVM-GSO (Support Vector Machines – Glowworm Swam Optimization), PSO-CNN (Particle Swarm Optimization-Convolutional Neural Networks), CNN (Convolutional Neural Networks), DCNN-EMFO (Deep Convolutional Neural Networks- Enhanced Moth Flame Optimization) and MACO-CNN (Modified Ant Colony Optimization–CNN). The APTOS DR Dataset utilized for the proposed metodology. The effort is made for identifying the DR stages by using iGWO. Finally, the results confirm that iGWO-FFOCNN technique yields good performance than compared to the existing techniques in relation to F-measure, precision, recall and also in DSC (Dice Similarity Coefficient), JSC (Jaccard Similarity Coefficient) and time period.

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Section: Resultsmentioning

confidence: 99%

improved grey wolf optimization algorithm (iGWO) for the detection of diabetic retinopathy using convnets and region based segmentation techniques

Vijayalakshmi¹,

Kumar²

2022

ijhs

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show abstract

“…The authors in Sridhar et al ( 15 ) have proposed an automatic system for detecting DR by using Convolutional Neural Network (CNN). The proposed system was tested based on binary classification and used an image dataset that is available on the Kaggle website.…”

Section: Related Workmentioning

confidence: 99%

“…(11)(12)(13)(14)(15)(16)(17) (44-46) depict these evaluation measurements. accuracy = TP + TN TP + TN + FN + FP (…”

mentioning

confidence: 99%

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Albadr

Ayob

Tiun

et al. 2022

Front. Public Health

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Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) is one of the most popular ML algorithms, and can be considered as an accurate algorithm in the process of classification, but has not been used in solving DR detection. Therefore, this work aims to apply the GWO-ELM classifier and employ one of the most popular features extractions, Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA), to increase the accuracy of DR detection system. Although the HOG-PCA has been tested in many image processing domains including medical domains, it has not yet been tested in DR. The GWO-ELM can prevent overfitting, solve multi and binary classifications problems, and it performs like a kernel-based Support Vector Machine with a Neural Network structure, whilst the HOG-PCA has the ability to extract the most relevant features with low dimensionality. Therefore, the combination of the GWO-ELM classifier and HOG-PCA features might produce an effective technique for DR classification and features extraction. The proposed GWO-ELM is evaluated based on two different datasets, namely APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD), in both binary and multi-class classification. The experiment results have shown an excellent performance of the proposed GWO-ELM model where it achieved an accuracy of 96.21% for multi-class and 99.47% for binary using APTOS-2019 dataset as well as 96.15% for multi-class and 99.04% for binary using IDRiD dataset. This demonstrates that the combination of the GWO-ELM and HOG-PCA is an effective classifier for detecting DR and might be applicable in solving other image data types.

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“…Research has shown that about 300 million people suffer from diabetic retinopathy (230 million in developing countries and 70 million in industrialized countries), and this figure could well double by 2025 [35] – [38] .…”

Section: Diabetic Retinopathy Applicationmentioning

confidence: 99%

Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy

Ghouali

Onyema

Guellil

et al. 2022

IEEE Open J. Eng. Med. Biol.

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Diabetic Retinopathy (DR) is one of the leading causes of blindness for people who have diabetes in the world. However, early detection of this disease can essentially decrease its effects on the patient. The recent breakthroughs in technologies, including the use of smart health systems based on Artificial intelligence, IoT and Blockchain are trying to improve the early diagnosis and treatment of diabetic retinopathy. In this study, we presented an AI-based smart teleopthalmology application for diagnosis of diabetic retinopathy. The app has the ability to facilitate the analyses of eye fundus images via deep learning from the Kaggle database using Tensor Flow mathematical library. The app would be useful in promoting mHealth and timely treatment of diabetic retinopathy by clinicians. With the AI-based application presented in this paper, patients can easily get supports and physicians and researchers can also mine or predict data on diabetic retinopathy and reports generated could assist doctors to determine the level of severity of the disease among the people.

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WITHDRAWN: Diabetic retinopathy detection using convolutional nueral networks algorithm

Cited by 11 publications

References 8 publications

improved grey wolf optimization algorithm (iGWO) for the detection of diabetic retinopathy using convnets and region based segmentation techniques

improved grey wolf optimization algorithm (iGWO) for the detection of diabetic retinopathy using convnets and region based segmentation techniques

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy

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