The Classification of Hypertensive Retinopathy using Convolutional Neural Network

Triwijoyo, Bambang Krismono; Budiharto, Widodo; Abdurachman, Edi

doi:10.1016/j.procs.2017.10.066

Cited by 35 publications

(18 citation statements)

References 8 publications

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“…In recent years, deep learning methods are increasingly used to improve clinical practice by using medical images including retinal fundus images [9,12,15]. The performance of these automated models could achieve as accurate as and in some cases superior to human experts in diagnosing diseases [14,15,25,33,34]. Triwijoyo et al [33] developed a model of predicting HR, which achieved the prediction accuracy of 0.986.…”

Section: Discussionmentioning

confidence: 99%

“…The performance of these automated models could achieve as accurate as and in some cases superior to human experts in diagnosing diseases [14,15,25,33,34]. Triwijoyo et al [33] developed a model of predicting HR, which achieved the prediction accuracy of 0.986. In detecting DR, there were also several studies achieved good performance with AUC > 0.989 [14,25,33,34,25].…”

Section: Discussionmentioning

confidence: 99%

“…Triwijoyo et al [33] developed a model of predicting HR, which achieved the prediction accuracy of 0.986. In detecting DR, there were also several studies achieved good performance with AUC > 0.989 [14,25,33,34,25]. However, these studies were focused on the complications of eyes caused by cardiovascular diseases.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China

Zhang

Yuan

et al. 2020

PLoS ONE

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Retinal fundus photography provides a non-invasive approach for identifying early microcirculatory alterations of chronic diseases prior to the onset of overt clinical complications. Here, we developed neural network models to predict hypertension, hyperglycemia, dyslipidemia, and a range of risk factors from retinal fundus images obtained from a cross-sectional study of chronic diseases in rural areas of Xinxiang County, Henan, in central China. 1222 high-quality retinal images and over 50 measurements of anthropometry and biochemical parameters were generated from 625 subjects. The models in this study achieved an area under the ROC curve (AUC) of 0.880 in predicting hyperglycemia, of 0.766 in predicting hypertension, and of 0.703 in predicting dyslipidemia. In addition, these models can predict with AUC>0.7 several blood test erythrocyte parameters, including hematocrit (HCT), mean corpuscular hemoglobin concentration (MCHC), and a cluster of cardiovascular disease (CVD) risk factors. Taken together, deep learning approaches are feasible for predicting hypertension, dyslipidemia, diabetes, and risks of other chronic diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China

Zhang

Yuan

et al. 2020

PLoS ONE

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“…The classification of hypertensive retinopathy using deep learning was conducted by (Triwijoyo et al, 2017). The model and dataset used are Convolutional Neural Network (CNN) and DRIVE dataset, with an accuracy of 98.6%.…”

Section: Related Workmentioning

confidence: 99%

Restricted Boltzmann Machines for Fundus Image Reconstruction and Classification of Hypertension Retinopathy

Triwijoyo

Sabarguna²,

Budiharto

et al. 2021

Journal of Computer Science

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Conventionally classification of hypertensive retinopathy through analysis of fundus images by experts, but this method the results are highly dependent on the accuracy of observations and expert experience. In this study, we propose a fundus image reconstruction and Hypertensive retinopathy classification model using Restricted Boltzmann Machines (RBM), as well as the Messidor database that has been labeled as a dataset. The experimental results show that the performance of the model produces an accuracy level of 99.05% where the model can generalize image input into one of the nine classes of the severity of hypertension retinopathy.

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“…The next model of the diagnostic system is based on the texture of retinal fundus imagery, as did Triwijoyo et.al [11]. The research is resized the image, then converted into CSF format and classified with Convolutional Neural Network.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Early Hypertensive Retinopathy using Fractal Analysis of Retinal Fundus Image

2018

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The Classification of Hypertensive Retinopathy using Convolutional Neural Network

Cited by 35 publications

References 8 publications

Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China

Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China

Restricted Boltzmann Machines for Fundus Image Reconstruction and Classification of Hypertension Retinopathy

Assessment of Early Hypertensive Retinopathy using Fractal Analysis of Retinal Fundus Image

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