Improvement of the Application of Diabetic Retinopathy Detection Model

Pang, Hao; Luo, Chang; Wang, Cong

doi:10.1007/s11277-018-5465-3

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…In this study, image processing is used for the histogram equalization, and thus it limits the techniques for the contrast developments. Pang et al (2018) proposed an application for the diabetic retinopathy model. The advanced method is used to visualize the results further, and the message are been queued to increase web service.…”

Section: Related Workmentioning

confidence: 99%

Threshold segmentation based multi-layer analysis for detecting diabetic retinopathy using convolution neural network

Shanthini

Manogaran

Vadivu

et al. 2021

J Ambient Intell Human Comput

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Diabetic retinopathy (DR) syndrome affects the vision of the eyes by damaging the blood vessels. Fore-hand detection and prevention of this syndrome are most significant as it results in vision blindness. Diagnosis and procedural analysis of this syndrome with modern healthcare science and technology are aided through artificial intelligence and processing units. In this article, a threshold segmentation based DR detection method is introduced. This method is keen is classifying the foreground and background of the input retinal image and processing through pixel-based segmentation. The process of assessing the layers is augmented using a two-layer convolutional neural network (CNN) that mitigates the false positives during classification. This process is sequential in determining the precise detection of the infected region of the retina. Besides, the segment-based CNN (S-CNN) handles the flaw in diagnosis through two-hidden layers for differentiating the threshold and normalized conditions based on classification. The proposed method is reliable in achieving better accuracy of detection, sensitivity, and true positives.

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Section: Related Workmentioning

confidence: 99%

Threshold segmentation based multi-layer analysis for detecting diabetic retinopathy using convolution neural network

Shanthini

Manogaran

Vadivu

et al. 2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…It is evident that deep learning is a powerful tool in today's artificial intelligence-based tasks. Recently, one deep learning method, i.e., convolutional neural network (CNN), has produced impressive results in biomedical imaging and CAD systems, e.g., cancer, brain tumor, and retinopathy detection [139,140]. Since DR is a life-threatening disease and requires early diagnosis to control its prevalence in patients, computerized tools have proven to be effective in early DR assessment, however, a gap remains regarding fast and real time solutions for DR prediction.…”

Section: Latest Trendsmentioning

confidence: 99%

“…Each neuron outcome is then mixed to maintain overlapping among input areas to better represent the original image information. This procedure is pursued for all layers until desirable results are achieved [135][136][137][138][139][140][141][142]. [145] CNN model Detection of exudates -- [113] Multiscale and CNN Detection of fovea and OD -AC: 97%…”

Section: Latest Trendsmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

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Diabetic retinopathy detection and stage classification in eye fundus images using active deep learning

Qureshi

Abbas

2021

Multimed Tools Appl

109

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Improvement of the Application of Diabetic Retinopathy Detection Model

Cited by 6 publications

References 4 publications

Threshold segmentation based multi-layer analysis for detecting diabetic retinopathy using convolution neural network

Threshold segmentation based multi-layer analysis for detecting diabetic retinopathy using convolution neural network

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Diabetic retinopathy detection and stage classification in eye fundus images using active deep learning

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