MVDRNet: Multi-view diabetic retinopathy detection by combining DCNNs and attention mechanisms

Luo, Xiaoling; Pu, Zuhui; Xu, Yong; Wong, Wai Keung; Su, Jingyong; Dou, Xiaoyan; Ye, Baikang; Hu, Jiying; Mou, Lisha

doi:10.1016/j.patcog.2021.108104

Cited by 41 publications

(28 citation statements)

References 22 publications

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“…In this section, the comparison process is carried out between the proposed ABCDM‐ESLO technique and three existing approaches namely ADL‐CNN, 12 PMNPDR, 13 and MVDRNet 17 models. The performances of these techniques are evaluated using different measures such as accuracy, sensitivity, specificity, and f‐score.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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ESLO: Enhanced sea lion optimization based bi‐directional CNN‐RNN for accurate detection of diabetic retinopathy

Vinayaki

Kalaiselvi²

2022

Concurrency and Computation

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Summary Diabetic retinopathy is considered to be one of the major causes of eye disorder which commonly occurs in working age group of people worldwide. Deficiency of proper screening and early diagnosing leads the diabetic patients completely affected by vision loss. Therefore, an effective detection of diabetic retinopathy before visual symptoms reduces the impact of visual impairment. In recent times, numerous research methods are adopted by researchers in this domain to automatically detect the existence of eye diseases; however, they provide only limited performances like less accuracy with high training error. Therefore, in this article, an attention based bi‐directional convolutional neural network‐recurrent neural network model along with enhanced sea lion optimization (ABCDM‐ESLO) technique is proposed for accurate detection and classification of healthy and unhealthy diabetic retinopathy cases based on the severity levels. In this, seven different datasets are used, analyzed, and classified into five categories (i.e., normal, mild, moderate, severe, and proliferative). Moreover, the performance metrics such as accuracy, sensitivity, specificity, and f‐score are evaluated with respect to severity levels thereby determining the efficiency of the proposed method. The performance of the proposed technique is examined by comparing it with other existing methods. The proposed method achieves 99.2% of detection accuracy, 94% of sensitivity, 96% of specificity, and 92.75% of f‐score. This reveals that the proposed ABCDM‐ESLO technique performs well and classifies than other existing methods in terms of different performance metrics.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…Luo et al 17 developed the multi‐view DR detection model (MVDRNet) which was a combination of deep CNN and attention mechanism “Shared Net”. The implementation of attention mechanism in the model enhances the performance of DR detection.…”

Section: Related Workmentioning

confidence: 99%

ESLO: Enhanced sea lion optimization based bi‐directional CNN‐RNN for accurate detection of diabetic retinopathy

Vinayaki

Kalaiselvi²

2022

Concurrency and Computation

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“…This limits the scope of these engineering methods' usefulness in a therapeutic setting. Deep learning techniques are introduced as a means of overcoming the limitations of machine learning and have significantly improved methods for analyzing medical images [19,20]. Therefore, in this study, a hybrid deep and machine learning-based architecture is presented as a pre-test to aid clinical professionals by enhancing the testing procedure in cases of DR which reduces money and effort.…”

Section: Introductionmentioning

confidence: 99%

LuNet-LightGBM: An Effective Hybrid Approach for Lesion Segmentation and DR Grading

Bapatla¹,

Harikiran²

2023

Computer Systems Science and Engineering

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Diabetes problems can lead to an eye disease called Diabetic Retinopathy (DR), which permanently damages the blood vessels in the retina. If not treated early, DR becomes a significant reason for blindness. To identify the DR and determine the stages, medical tests are very labor-intensive, expensive, and timeconsuming. To address the issue, a hybrid deep and machine learning techniquebased autonomous diagnostic system is provided in this paper. Our proposal is based on lesion segmentation of the fundus images based on the LuNet network. Then a Refined Attention Pyramid Network (RAPNet) is used for extracting global and local features. To increase the performance of the classifier, the unique features are selected from the extracted feature set using Aquila Optimizer (AO) algorithm. Finally, the LightGBM model is applied to classify the input image based on the severity. Several investigations have been done to analyze the performance of the proposed framework on three publically available datasets (MESSIDOR, APTOS, and IDRiD) using several performance metrics such as accuracy, precision, recall, and f1-score. The proposed classifier achieves 99.29%, 99.35%, and 99.31% accuracy for these three datasets respectively. The outcomes of the experiments demonstrate that the suggested technique is effective for disease identification and reliable DR grading.

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“…Compared with step-by-step learning, the end-to-end deep learning method does not need to label data manually before executing each independent learning task, but directly learn the mapping from the original data to the required output. Luo et al 12 proposed a convolutional network that fuses multi-view fundus images to make full use of the pathological features of the retina. The attention mechanism module was added to the network to increase the attention onto the important features in the fundus image.…”

Section: Introductionmentioning

confidence: 99%

URNet: System for recommending referrals for community screening of diabetic retinopathy based on deep learning

Yang

Liu

et al. 2023

Exp Biol Med (Maywood)

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Diabetic retinopathy (DR) will cause blindness if the detection and treatment are not carried out in the early stages. To create an effective treatment strategy, the severity of the disease must first be divided into referral-warranted diabetic retinopathy (RWDR) and non-referral diabetic retinopathy (NRDR). However, there are usually no sufficient fundus examinations due to lack of professional service in the communities, particularly in the developing countries. In this study, we introduce UGAN_Resnet_CBAM (URNet; UGAN is a generative adversarial network that uses Unet for feature extraction), a two-stage end-to-end deep learning technique for the automatic detection of diabetic retinopathy. The characteristics of DDR fundus data set were used to design an adaptive image preprocessing module in the first stage. Gradient-weighted Class Activation Mapping (Grad-CAM) and t-distribution and stochastic neighbor embedding (t-SNE) were used as the evaluation indices to analyze the preprocessing results. In the second stage, we enhanced the performance of the Resnet50 network by integrating the convolutional block attention module (CBAM). The outcomes demonstrate that our proposed solution outperformed other current structures, achieving 94.5% and 94.4% precisions, and 96.2% and 91.9% recall for NRDR and RWDR, respectively.

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MVDRNet: Multi-view diabetic retinopathy detection by combining DCNNs and attention mechanisms

Cited by 41 publications

References 22 publications

ESLO: Enhanced sea lion optimization based bi‐directional CNN‐RNN for accurate detection of diabetic retinopathy

ESLO: Enhanced sea lion optimization based bi‐directional CNN‐RNN for accurate detection of diabetic retinopathy

LuNet-LightGBM: An Effective Hybrid Approach for Lesion Segmentation and DR Grading

URNet: System for recommending referrals for community screening of diabetic retinopathy based on deep learning

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