Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy

Kar, Sudeshna Sil; Maity, Santi P.

doi:10.1109/tbme.2017.2707578

Cited by 126 publications

(34 citation statements)

References 25 publications

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“…For grouping reason, assortment of feature classifiers are examined, for example, GMM, KNN, SVM, Adaboost and hybrid classifiers, and it is discovered that for the bright lesions order GMM classifier and for the red sore characterization KNN is the best classifiers. A multi lesion, for example, MA, HEM and exudates discovery framework is proposed by Kar et al [21] where preprocessing is done independently for the dark and bright sore, for that curvlet based edge enhancement system is utilized for dark sores and ideal band pass filter for red. Applicant lesions identification is performed in the third step, for which matched filter and Laplacian of Gaussian separating is utilized.…”

Section: E Multi Lesion Detection (Bright and Red Lesions)mentioning

confidence: 99%

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Jebran P*,

Gupta

2020

IJRTE

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In this paper existing writing for computer added diagnosis (CAD) based identification of lesions that might be connected in the early finding of Diabetic Retinopathy (DR) is talked about. The recognition of sores, for example, Microaneurysms (MA), Hemorrhages (HEM) and Exudates (EX) are incorporated in this paper. A range of methodologies starting from conventional morphology to deep learning techniques have been discussed. The different strategies like hand crafted feature extraction to automated CNN based component extraction, single lesion identification to multi sore recognition have been explored. The different stages in each methods beginning from the image preprocessing to classification stage are investigated. The exhibition of the proposed strategies are outlined by various performance measurement parameters and their used data sets are tabulated. Toward the end we examined the future headings.

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Section: E Multi Lesion Detection (Bright and Red Lesions)mentioning

confidence: 99%

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Jebran P*,

Gupta

2020

IJRTE

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“…5,11,24,36,38 Analyzing eye image is a hard task given that the disease often has few symptoms. Some recent studies have reported that it is possible to avoid visual loss if DR is analyzed at early stage.…”

Section: Diabetic Retinopathy Detection In Eye Imagesmentioning

confidence: 99%

“…Some recent studies have reported that it is possible to avoid visual loss if DR is analyzed at early stage. 5,11,24,36,38 Analyzing eye image is a hard task given that the disease often has few symptoms. On the other side, the current protocol followed by the most qualified experts is exhaustive and time consuming.…”

Section: Diabetic Retinopathy Detection In Eye Imagesmentioning

confidence: 99%

Bayesian inference framework for bounded generalized Gaussian‐based mixture model and its application to biomedical images classification

Alroobaea

Rubaiee

Bourouis

et al. 2019

Int J Imaging Syst Tech

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Biomedical image classification problem has attracted a lot of attention in medical engineering community and medicine applications. Accurate and automatic classification (eg, normal/abnormal or malignant/benign) has a variety of applications such as automatic decision making and is known to be very challenging. In this research, we address this problem by investigating the effectiveness of Bayesian inference methods for statistical bounded mixture models. Indeed, a novel approach termed as Bayesian learning for bounded generalized Gaussian mixture models is developed. The consideration of bounded mixture models is encouraged by their capability to take into account the nature of the data that is compactly supported. Furthermore, the consideration of Bayesian inference is more attractive compared to frequentist reasoning. In this work, we address main issues related to accurate data classification such as the effective estimation of the model's parameters and the selection of the optimal model complexity. Moreover, the problem of over-or under-fitting is treated by taking into account the uncertainty through introducing prior information about the model's parameters. A comparative study between different Gaussian-based models is also performed to evaluate the performance of the proposed framework. Experiments have been conducted on challenging biomedical image datasets that involve retinal images for diabetic retinopathy detection and mammograms for breast cancer detection. Obtained results are encouraging and show the benefits of our Bayesian framework. K E Y W O R D SBayesian inference, biomedical imaging, bounded mixture models, generalized Gaussian distribution, image classification, Markov chain Monte Carlo (MCMC)

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“…Many of these AI systems utilize deep learning, [3][4][5][6][7][8][9] although there are groups developing automated methods that do not use deep learning. [10][11][12] The performance of automated systems in general is impressive and often exceeds the performance of human graders.…”

Section: Conclusion and Relevancementioning

confidence: 99%

Development of an artificial intelligence system to classify pathology and clinical features on retinal fundus images

Stevenson

Hong

Ogbuehi

2018

Clinical Exper Ophthalmology

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Importance Artificial intelligence (AI) algorithms are under development for use in diabetic retinopathy photo screening pathways. To be clinically acceptable, such systems must also be able to classify other fundus abnormalities and clinical features at the point of care. Background We aimed to develop an AI system that can detect several fundus pathologies and report relevant clinical features. Design Convolutional neural network training with retrospective data set. Participants Colour fundus photos were obtained from publicly available fundus image databases. Methods Images were uploaded to a web‐based AI platform for training and validation of AI classifiers. Separate classifiers were created for each fundus pathology and clinical feature. Main Outcome Measures Accuracy, sensitivity, specificity and area under receiver operating characteristic curve (AUC) for each classifier. Results We obtained 4435 images from publicly available fundus image databases. AI classifiers were developed for each disease state above. Although statistical performance was limited by the small sample size, average accuracy was 89%, average sensitivity was 75%, average specificity was 89% and average AUC was 0.58. Conclusion and Relevance This study is a proof‐of‐concept AI system that could be implemented within a diabetic photo‐screening pathway. Performance was promising but not yet at the level that would be required for clinical application. We have shown that it is possible for clinicians to develop AI classifiers with no previous programming or AI knowledge, using standard laptop computers.

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Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy

Abstract: Extensive simulations on different publicly available databases highlight an improved performance over the existing methods with an average accuracy of and robustness in detecting the various types of DR lesions irrespective of their intrinsic properties.

Cited by 126 publications

References 25 publications

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Bayesian inference framework for bounded generalized Gaussian‐based mixture model and its application to biomedical images classification

Development of an artificial intelligence system to classify pathology and clinical features on retinal fundus images

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