Attentional Mechanisms and Improved Residual Networks for Diabetic Retinopathy Severity Classification

Cao, Juan; Chen, Jiaran; Zhang, Xinying; Yan, Qifeng; Zhao, Yitian

doi:10.1155/2022/9585344

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Because relevant lesion marks in fundus images usually vary in size, some studies [89,90] have also applied inception modules to extract features at different resolutions. Cao et al [91] used ResNet as the backbone network for DR severity classification. The attention module was applied in this architecture to improve the feature extraction and enhance the model performance.…”

Section: A Cnn and Related Modelsmentioning

confidence: 99%

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Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

Zhu,

Xiong,

Zhong

et al. 2024

IEEE Access

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Diabetic retinopathy (DR) is a microvascular disease that is associated with diabetes mellitus. DR can cause irreversible vision loss and blindness. DR classification, that is, early DR diagnosis and accurate DR grading, is critical for vision protection and immediate treatment. Deep learning-based automated systems led to significant expectations for DR classification based on fundus images with several advantages. In the past several years, many outstanding studies in this area have been conducted and several review articles have been published. However, the new trends and the future directions are need to further analyzed. Thus, we carefully included and read 94 related articles published from 2018 to 2023 through Web of Science, PubMed, Scopus, and IEEE Xplore. From this review, we found that transfer learning has been used as an outstanding strategy for overcoming the issue of the limited data resources to support DR analysis. CNN models of ResNet and VGGNet with layers of tens or even hundreds are the most popular frameworks used for DR classification. The APTOS 2019 and EyePACS are the most widely used datasets for DR classification. In addition, some lightweight DL architectures like SqueezeNet and MobileNet have been proposed for DR classification tasks, especially for limited data resources and computational capabilities. Although deep learning has achieved or surpassed human-level accuracy in DR classification, there is still a long way to go in real clinical workflows. Further improvements in model interpretability, trustworthiness from ophthalmologists, cost-effective and reliable DR screening systems are needed.

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Section: A Cnn and Related Modelsmentioning

confidence: 99%

“…In 2022, Cao et al [91] proposed a ResNet-based networkworkable scheme for DR classification. In this model, the feature information of the hidden layer can be enhanced by modifying the structure of the residual blocks and by adding an attention mechanism.…”

Section: B Multi-class Classification For Gradingmentioning

confidence: 99%

Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

Zhu,

Xiong,

Zhong

et al. 2024

IEEE Access

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“…Their methodology consisted of augmenting the dataset using image processing methods to enhance contrast. The study highlighted that the attention mechanisms led to more effective feature extraction, resulting in an accuracy of 91.3% and a kappa value of 89.3% [ 58 ]. [ 39 ] Performed an extensive analysis, including research that used machine learning and deep learning algorithms to identify diabetic retinopathy.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification

Khan,

Sun,

Khalid

et al. 2024

PLoS ONE

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In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant contributor to vision loss globally, through a pioneering approach. We propose a novel integration of a Genetic Grey Wolf Optimization (G-GWO) algorithm with a Fully Convolutional Encoder-Decoder Network (FCEDN), further enhanced by a Kernel Extreme Learning Machine (KELM) for refined image segmentation and disease classification. This innovative combination leverages the genetic algorithm and grey wolf optimization to boost the FCEDN’s efficiency, enabling precise detection of DED stages and differentiation among disease types. Tested across diverse datasets, including IDRiD, DR-HAGIS, and ODIR, our model showcased superior performance, achieving classification accuracies between 98.5% to 98.8%, surpassing existing methods. This advancement sets a new standard in DED detection and offers significant potential for automating fundus image analysis, reducing reliance on manual examination, and improving patient care efficiency. Our findings are crucial to enhancing diagnostic accuracy and patient outcomes in DED management.

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Detection of Diabetic Retinopathy Severity by Image Classification Using a Hybrid Model and Attention Mechanism

Nunna,

Tiwari

2024

Proceedings of the 7th International Conference on Advance Computing and Intelligent Engineering

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Attentional Mechanisms and Improved Residual Networks for Diabetic Retinopathy Severity Classification

Cited by 7 publications

References 17 publications

Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification

Detection of Diabetic Retinopathy Severity by Image Classification Using a Hybrid Model and Attention Mechanism

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