Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Images

Phan, Thanh Vân; Séoud, Lama; Chakor, Hadi; Cheriet, Foued

doi:10.1155/2016/5893601

Cited by 31 publications

(19 citation statements)

References 40 publications

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“…Most of these studies focused on the grading severity of diseases. Previously published papers claim that a concept of multi-categorical classification was applied to predict AMD progression by using SVM and RF [ 53 ]. Multiclass SVM also worked well in classification of DMR severity [ 54 ].…”

Section: Discussionmentioning

confidence: 99%

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

et al. 2017

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Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen’s kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

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Section: Discussionmentioning

confidence: 99%

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

et al. 2017

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“…al. 3 Phan et al 5 Burlina et al 29 Accuracy late wet AMD, sensitivity was 59% for 1 year and 60% for 2 years; the specificity was 68% for 1 year and 70% for 2 years. ( Supplementary Table S15, online supplement).…”

Section: Metricmentioning

confidence: 99%

Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD

Bhuiyan

Wong

Ting

et al. 2020

Trans. Vis. Sci. Tech.

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Citation: Bhuiyan A, Wong TY, Ting DSW, Govindaiah A, Souied EH, Smith RT. Artificial intelligence to stratify severity of age-related macular degeneration (AMD) and predict risk of progression to late AMD . Trans Vis Sci Tech. 2020;9(2):25, https://doi.org/10. 1167/tvst.9.2.25 Purpose: To build and validate artificial intelligence (AI)-based models for AMD screening and for predicting late dry and wet AMD progression within 1 and 2 years. Methods:The dataset of the Age-related Eye Disease Study (AREDS) was used to train and validate our prediction model. External validation was performed on the Nutritional AMD Treatment-2 (NAT-2) study. FirstStep: An ensemble of deep learning screening methods was trained and validated on 116,875 color fundus photos from 4139 participants in the AREDS study to classify them as no, early, intermediate, or advanced AMD and further stratified them along the AREDS 12 level severity scale. Second step: the resulting AMD scores were combined with sociodemographic clinical data and other automatically extracted imaging data by a logistic model tree machine learning technique to predict risk for progression to late AMD within 1 or 2 years, with training and validation performed on 923 AREDS participants who progressed within 2 years, 901 who progressed within 1 year, and 2840 who did not progress within 2 years. For those found at risk of progression to late AMD, we further predicted the type (dry or wet) of the progression of late AMD.Results: For identification of early/none vs. intermediate/late (i.e., referral level) AMD, we achieved 99.2% accuracy. The prediction model for a 2-year incident late AMD (any) achieved 86.36% accuracy, with 66.88% for late dry and 67.15% for late wet AMD. For the NAT-2 dataset, the 2-year late AMD prediction accuracy was 84%. Conclusions:Validated color fundus photo-based models for AMD screening and risk prediction for late AMD are now ready for clinical testing and potential telemedical deployment.Translational Relevance: Noninvasive, highly accurate, and fast AI methods to screen for referral level AMD and to predict late AMD progression offer significant potential improvements in our care of this prevalent blinding disease.

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“…By using a robust Haralick set of features (averaged across different displacements and angles) and a machine learning algorithm, we determined the most important texture features and their combinations with intensity features for drusen diagnosis. Finally, instead of using only the green channel as in [37] and [53] where local binary patterns (LBP) features computed in green channel where reported to be the most important features in distinguishing drusen from non-drusen images, we showed that hyper-spectral imaging has the potential to provide the optimal combination of texture and intensity features for drusen ROIs characterization.…”

Section: Discussiomentioning

confidence: 90%

Drusen diagnosis comparison between hyper-spectral and color retinal images

Wang

Soetikno

Furst

et al. 2019

Biomed. Opt. Express

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Age-related macular degeneration (AMD) is a degenerative aging disorder, which can lead to irreversible vision loss in older individuals. The emergence of clinical applications of retinal hyper-spectral imaging provides a unique opportunity to capture important spectral signatures, with the potential to enhance the molecular diagnosis of retinal diseases. In this study, we use a machine learning classification approach to explore whether hyper-spectral images offer an improved outcome compared to standard RGB images. Our results show that the classifier performs better on hyper-spectral images with improved accuracy and sensitivity for drusen classification compared to standard imaging. By examining the most important features in the classification task, our data suggest that drusen are highly heterogeneous. Our work provides further evidence that hyper-spectral retinal image data are uniquely suited for computer-aided diagnosis and detection techniques.

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Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Images

Cited by 31 publications

References 40 publications

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

Artificial Intelligence to Stratify Severity of Age-Related Macular Degeneration (AMD) and Predict Risk of Progression to Late AMD

Drusen diagnosis comparison between hyper-spectral and color retinal images

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