Exudates and optic disk detection in retinal images of diabetic patients

Zeljković, V.; Bojic, Milena; Zhao, Shengwei; Tameze, Claude; Valev, Ventzeslav

doi:10.1002/cpe.3215

Cited by 8 publications

(6 citation statements)

References 22 publications

(49 reference statements)

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“…Improving representation ability is a crucial problem in designing the model structure for handling complex tasks such as attribute analysis. Typical image processing methods such as contrast enhancement 8 , histogram analysis 9 , edge detection 10 , and matched filtering 11 have been applied to identify different types of DR lesions.…”

Section: Literature Reviewmentioning

confidence: 99%

Weakly Supervised Sensitive Heatmap framework to classify and localize diabetic retinopathy lesions

Al-Mukhtar

Morad

et al. 2021

Sci Rep

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Vision loss happens due to diabetic retinopathy (DR) in severe stages. Thus, an automatic detection method applied to diagnose DR in an earlier phase may help medical doctors to make better decisions. DR is considered one of the main risks, leading to blindness. Computer-Aided Diagnosis systems play an essential role in detecting features in fundus images. Fundus images may include blood vessels, exudates, micro-aneurysm, hemorrhages, and neovascularization. In this paper, our model combines automatic detection for the diabetic retinopathy classification with localization methods depending on weakly-supervised learning. The model has four stages; in stage one, various preprocessing techniques are applied to smooth the data set. In stage two, the network had gotten deeply to the optic disk segment for eliminating any exudate's false prediction because the exudates had the same color pixel as the optic disk. In stage three, the network is fed through training data to classify each label. Finally, the layers of the convolution neural network are re-edited, and used to localize the impact of DR on the patient's eye. The framework tackles the matching technique between two essential concepts where the classification problem depends on the supervised learning method. While the localization problem was obtained by the weakly supervised method. An additional layer known as weakly supervised sensitive heat map (WSSH) was added to detect the ROI of the lesion at a test accuracy of 98.65%, while comparing with Class Activation Map that involved weakly supervised technology achieved 0.954. The main purpose is to learn a representation that collect the central localization of discriminative features in a retina image. CNN-WSSH model is able to highlight decisive features in a single forward pass for getting the best detection of lesions.

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Section: Literature Reviewmentioning

confidence: 99%

Weakly Supervised Sensitive Heatmap framework to classify and localize diabetic retinopathy lesions

Al-Mukhtar

Morad

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Improving the representation ability is a crucial problem in designing the model structure for handling complex tasks such as attribute analysis. Typical image processing methods such as contrast enhancement [8], histogram analysis [9], edge detection [10], and matched filtering [11] have been applied to identify different types of DR lesions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The gradient g(x, y) is a threshold version of f(x, y) at some global threshold T. The global thresholding applied for the effective region outputs in a packed bit (0, 1) format described in Eq. (8). The Global thresholding is creative as the degree of intensity separation between the two peaks represents the image's background and object pixels.…”

Section: Optic Disk Removalmentioning

confidence: 99%

WSSH_DR: Weakly Supervised Sensitive Heatmap Framework to Classify and Localize Diabetic Retinopathy Lesions

Al-Mukhtar¹,

Morad²,

M³

et al. 2021

Preprint

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Vision loss happens due to diabetic retinopathy (DR) in severe stages. Thus, an automatic detection method applied to diagnose DR in an earlier phase may help medical doctors to make better decisions. DR is considered one of the main risks, leading to blindness. Computer-Aided Diagnosis (CAD) systems play an essential role in detecting features in fundus images. Fundus images may include blood vessel area, exudates, micro-aneurysm, hemorrhages, and neovascularization. In this paper, our model combines automatic detection for the diabetic retinopathy classification with localization methods depending on weakly-supervised learning. The model has four stages; in stage one, various preprocessing techniques are applied for smoothing the data set. In stage two, the network had gotten deeply to the optic disk segment for eliminating any exudate's false prediction because the exudates had the same color pixel as the optic disk. Stage three, the network is fed through training data to classify each class label. Finally, the layers of the convolution neural network are re-edited, and the layers are used to localize the impact of DR on the eye's patient. The framework tackled the matching technique between two essential concepts where the classification problem depends on the supervised learning method. In comparison, the localization problem was obtained by the weakly supervised method. An additional layer known as weakly supervised sensitive heat map (WSSH) was added to detect the ROI of the lesion at a test accuracy of 98.65%.

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“…The work proposed by Zeljkovic et al [144] falls within the realm of image processing and visualization. They propose cost-effective algorithms for retinal image analysis in diabetic patients.…”

Section: High-performance Computing For Recent and New Application Domentioning

confidence: 99%

High‐performance computing: to boldly go where no human has gone before

Limet

Smari

Spalazzi

2015

Concurrency and Computation

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International audienceComputing and computational science are well on their way to enter into the exascale era while High Performance Computing (HPC) is now present in many spheres and domains of modern society. New technologies such as cloud computing, big data, and connected objects open many perspectives and possibilities that were unattainable until now. These are high times for computing and for HPC in particular. The High Performance Computing \& Simulation conference aims to bring together researchers working on various aspects of HPC, their design and use, and their applications. As has been the conference tradition, selected extended papers of HPCS 2012 are presented in this special issue. These should be interesting contributions that supplement the current state-of-the-art research in HPC. To relate these works and highlight their value, in this article, we briefly trace back the history of HPC, sketch the state of the present research and development in the field, and project some of the challenges and future trends anticipated

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Exudates and optic disk detection in retinal images of diabetic patients

Cited by 8 publications

References 22 publications

Weakly Supervised Sensitive Heatmap framework to classify and localize diabetic retinopathy lesions

Weakly Supervised Sensitive Heatmap framework to classify and localize diabetic retinopathy lesions

WSSH_DR: Weakly Supervised Sensitive Heatmap Framework to Classify and Localize Diabetic Retinopathy Lesions

High‐performance computing: to boldly go where no human has gone before

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