Detection of Diabetic Retinopathy Using Ensemble Learning Techniques

Dutta, Anirban; Agarwal, Parul; Mittal, Anushka; Khandelwal, Shishir; Mehta, Shikha

doi:10.1002/9781119821908.ch7

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Dutta et al (2023) have suggested detection of diabetic retinopathy using ensemble learning techniques. The development and comparison of three ensemble‐learning approaches: AdaNaive, AdaSVM, and Adaforest‐ with machine learning approaches for binary and multiple categorization of DR.…”

Section: Related Workmentioning

confidence: 99%

Micro aneurysm detection using optimized residual‐based temporal attention Convolutional Neural Network with Inception‐V3 transfer learning

Alotaibi

2024

Microscopy Res & Technique

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In this manuscript micro aneurysm detection using residual‐based temporal attention Convolutional Neural Network (CNN) with Inception‐V3 transfer learning optimized with equilibrium optimization algorithm (MA‐RTCNN‐Inception V3‐EOA) is proposed. The proposed research work contains four phases: (1) pre‐processing, (2) segmentation, (3) post‐processing, and (4) classification. At first, guided box filtering for contrast enhancement and background exclusion of input image. The proposed MA‐RTCNN‐Inception V3‐EOA based classification framework is implemented in MATLAB using several performances evaluating metrics like precision, sensitivity, f‐measure, specificity, accuracy, classification error rate, and Matthews's correlation coefficient and RoC analysis. The experimental outcome demonstrates that the proposed method provides 23.56%, 14.99%, and 21.37% higher accuracy and 31.26%, 57.69%, and 21.14% minimum classification error rate compared to existing methods, such as diabetic retinopathy identification utilizing prognosis of micro aneurysm and early diagnosis for non‐proliferative diabetic retinopathy depending on deep learning approaches (DRD‐CNN‐NPDR), a magnified adaptive feature pyramid network for automatic micro aneurysms identification (MAFPN‐AMD‐MAFP‐Net) respectively.Research Highlights Micro aneurysm detection using residual‐based temporal attention Convolutional Neural Network (CNN) is proposed. To get rid of the retina background, guided box filtering is applied. COAT is used for segmenting the images into smaller parts RTCNN is used for accurate micro aneurysms disease classification. RT‐CNN algorithm successfully identifies the micro aneurysms using EOA.

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Section: Related Workmentioning

confidence: 99%

Micro aneurysm detection using optimized residual‐based temporal attention Convolutional Neural Network with Inception‐V3 transfer learning

Alotaibi

2024

Microscopy Res & Technique

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“…They achieved in terms of overall classification accuracy 80.36%, but they did not consider "ungradable" class of images. Recently, Dutta et al [55] used three ensemble classifiers (AdaNaive, AdaSVM, and Adaforest) to enhance binary and multiclass classification of DR.…”

Section: Related Workmentioning

confidence: 99%

“…The goal is to demonstrate that such models provide significant differences in results between the individual models and the ensemble models. We used McNemar's test [55], a statistical test used on paired nominal data. McNemar's test is provided with a 2 × 2 contingency table (Figure 9).…”

Section: Statistical Significancementioning

confidence: 99%

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Image preprocessing‐based ensemble deep learning classification of diabetic retinopathy

Macsik,

Pavlovicova,

Kajan

et al. 2023

IET Image Processing

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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.

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A diagnosis model for detection and classification of diabetic retinopathy using deep learning

Syed,

M A

2023

Netw Model Anal Health Inform Bioinforma

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Detection of Diabetic Retinopathy Using Ensemble Learning Techniques

Cited by 6 publications

References 20 publications

Micro aneurysm detection using optimized residual‐based temporal attention Convolutional Neural Network with Inception‐V3 transfer learning

Micro aneurysm detection using optimized residual‐based temporal attention Convolutional Neural Network with Inception‐V3 transfer learning

Image preprocessing‐based ensemble deep learning classification of diabetic retinopathy

A diagnosis model for detection and classification of diabetic retinopathy using deep learning

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