A Systematic Review on Diabetic Retinopathy Detection Using Deep Learning Techniques

Vij, Richa; Arora, Sakshi

doi:10.1007/s11831-022-09862-0

Cited by 26 publications

(8 citation statements)

References 226 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detection of DR and vessels can be achieved using public and private data sets, including retinal imaging methods like OCTs and fundus images. OCT scans reveal retinal thickness and contour details, while fundus images are 2-dimensional ( Vij & Arora, 2023 ). The Table 2 includes details about the sample size, resolution, accessibility, data sources, and a list of fundus retinal data sets relevant to DR detection.…”

Section: Diabetic Retinopathymentioning

confidence: 99%

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Bhulakshmi,

Rajput

2024

PeerJ Computer Science

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

show abstract

Section: Diabetic Retinopathymentioning

confidence: 99%

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Bhulakshmi,

Rajput

2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…To reduce such data heterogeneity, which ultimately affects the performance of the classification model, as well as to highlight some fine details of the images, preprocessing methods of the fundus images were successfully introduced in various studies. As can be seen in many works, the best preprocessing method strongly depends on the combination of the characteristics of the used dataset and the proposed model [28][29][30][31][32][33].…”

Section: Related Workmentioning

confidence: 99%

“…Different preprocessing methods accentuate different image details. It may vary depending on the characteristics of used fundus dataset [28][29][30][31][32], described above in Related Works. For this reason and to achieve diverse data suitable for ensemble classification, we applied four different types of preprocessing on the original images.…”

Section: Preprocessing Of Imagesmentioning

confidence: 99%

Image preprocessing‐based ensemble deep learning classification of diabetic retinopathy

Macsik,

Pavlovicova,

Kajan

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

Diabetic retinopathy (DR) can cause irreversible eye damage, even blindness. The prognosis improves with early diagnosis. According to the International Classification of Diabetic Retinopathy Severity Scale (ICDRSS), DR has five stages. Modern, cost‐effective techniques for automatic DR screening and staging of fundus images are based on deep learning (DL). To obtain higher classification accuracy, the combination of several diverse individual DL models into one ensemble could be used. A new approach to model diversity in an ensemble is proposed by manipulating the training input data involving original and four variants of preprocessed image datasets. There are publicly available datasets with labels for all five stages, but some contain poor‐quality images. In contrast, this algorithm was trained on images from a six‐class DDR dataset, including the class of poor‐quality ungradable images, to enhance the classification performance. The solution was evaluated on the APTOS dataset, containing only ICDRSS classes. Classification results of the ensemble model were presented on two different ensemble convolutional neural network (CNN) models, based on Xception and EfficientNetB4 architectures using two fusion approaches. Our proposed ensemble models outperformed all other single deep learning architectures regarding overall accuracy and Cohen's Kappa, with the best results using the EfficientNetB4 architecture.

show abstract

“…Many researchers have applied the deep learning techniques to DR grading and achieved good results [22] [23] [24] , [25] , [26] , [27] , [28] , [29] . Grinsven et al [24] improve and boost the training by selecting misclassified negative samples.…”

Section: Related Workmentioning

confidence: 99%

Fine-grained attention & knowledge-based collaborative network for diabetic retinopathy grading

Tian

Wang

Sun

et al. 2023

Heliyon

View full text Add to dashboard Cite

A Systematic Review on Diabetic Retinopathy Detection Using Deep Learning Techniques

Cited by 26 publications

References 226 publications

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Image preprocessing‐based ensemble deep learning classification of diabetic retinopathy

Fine-grained attention & knowledge-based collaborative network for diabetic retinopathy grading

Contact Info

Product

Resources

About