An Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost

Zhang, Yinan; An, Mingqiang

doi:10.1155/2016/4345936

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…After completing qualitative synthesis of the 45 studies, we proceeded to only include ML studies that involved use of color fundus photograph for DR screening. After further exclusion, only 32 studies were retrieved after the second stage of literature selection [ 13 , 15 , 17 - 46 ]. The third stage of the literature search was performed in June 2020, and 28 out of 651 studies were retrieved after literature selection [ 47 - 74 ], resulting in a total of 60 (N=32+28) included studies for final analysis.…”

Section: Resultsmentioning

confidence: 99%

Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis

Wu¹,

Liu²,

Hsu³

et al. 2021

J Med Internet Res

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Background Diabetic retinopathy (DR), whose standard diagnosis is performed by human experts, has high prevalence and requires a more efficient screening method. Although machine learning (ML)–based automated DR diagnosis has gained attention due to recent approval of IDx-DR, performance of this tool has not been examined systematically, and the best ML technique for use in a real-world setting has not been discussed. Objective The aim of this study was to systematically examine the overall diagnostic accuracy of ML in diagnosing DR of different categories based on color fundus photographs and to determine the state-of-the-art ML approach. Methods Published studies in PubMed and EMBASE were searched from inception to June 2020. Studies were screened for relevant outcomes, publication types, and data sufficiency, and a total of 60 out of 2128 (2.82%) studies were retrieved after study selection. Extraction of data was performed by 2 authors according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), and the quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis of diagnostic accuracy was pooled using a bivariate random effects model. The main outcomes included diagnostic accuracy, sensitivity, and specificity of ML in diagnosing DR based on color fundus photographs, as well as the performances of different major types of ML algorithms. Results The primary meta-analysis included 60 color fundus photograph studies (445,175 interpretations). Overall, ML demonstrated high accuracy in diagnosing DR of various categories, with a pooled area under the receiver operating characteristic (AUROC) ranging from 0.97 (95% CI 0.96-0.99) to 0.99 (95% CI 0.98-1.00). The performance of ML in detecting more-than-mild DR was robust (sensitivity 0.95; AUROC 0.97), and by subgroup analyses, we observed that robust performance of ML was not limited to benchmark data sets (sensitivity 0.92; AUROC 0.96) but could be generalized to images collected in clinical practice (sensitivity 0.97; AUROC 0.97). Neural network was the most widely used method, and the subgroup analysis revealed a pooled AUROC of 0.98 (95% CI 0.96-0.99) for studies that used neural networks to diagnose more-than-mild DR. Conclusions This meta-analysis demonstrated high diagnostic accuracy of ML algorithms in detecting DR on color fundus photographs, suggesting that state-of-the-art, ML-based DR screening algorithms are likely ready for clinical applications. However, a significant portion of the earlier published studies had methodology flaws, such as the lack of external validation and presence of spectrum bias. The results of these studies should be interpreted with caution.

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Section: Resultsmentioning

confidence: 99%

Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis

Wu¹,

Liu²,

Hsu³

et al. 2021

J Med Internet Res

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“…In addition, the authors in Zhang and An ( 24 ) have proposed an automatic DR detection system. The proposed system uses two features extraction methods (i.e., lesion detection and anatomical part recognition) and Kernel Extreme Learning Machine (KELM) with an active learning technique for the classification process.…”

Section: Related Workmentioning

confidence: 99%

“…Although (23)(24)(25)(26) showed that the ELM and KELM outperformed their comparatives, these studies have ignored the fact that the random generated input weights and biases of the ELM and KELM need to be optimized. In other words, there is no guarantee that the trained ELM/KELM is the best for carrying out the classification.…”

Section: Related Workmentioning

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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“…In this method, the most informative instances for model training are selected for labeling, aiming to reduce cost, whereas maintaining an adequate model performance [26,35,37,38]. AL has been recently evaluated in the context of diabetic retinopathy, in which large amounts of unlabeled data generated by screening programs represent a use case for this technique [39,40]. This and other alternatives to reduce labeling costs (e.g., transfer learning and self-supervised learning) represent enabling techniques for research by clinicians with limited resources.…”

Section: Deep Learning Techniques Utilizing Fewer Labelsmentioning

confidence: 99%

Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization

Korot

Gonçalves

Khan

et al. 2021

Current Opinion in Ophthalmology

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An Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost

Cited by 8 publications

References 28 publications

Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis

Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis

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

Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization

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