Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading

Sahlsten, Jaakko; Jaskari, Joel; Kivinen, Jyri; Turunen, Lauri; Jaanio, Esa; Hietala, Kustaa; Kaski, Kimmo

doi:10.1038/s41598-019-47181-w

Cited by 196 publications

(137 citation statements)

References 12 publications

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“…The choice of image size was to minimize the computational load on the Microsoft Azure™ platform, while not compromising the performance of the trained CNNs. According to existing literature (40) and based on our experience, larger image sizes would have led to diminishing returns in accuracy and overall performance of the designed CNNs. The resized cropped images were enhanced by applying a Gaussian blur technique (41), using the equation below.…”

Section: Methodsologymentioning

confidence: 85%

Towards implementation of AI in New Zealand national screening program: Cloud-based, Robust, and Bespoke

Xie

Yang

Squirrell

et al. 2019

Preprint

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25Convolutional Neural Networks (CNN)s have become a prominent method of AI 26 implementation in medical classification tasks. Grading Diabetic Retinopathy (DR) has been 27 at the forefront of the development of AI for ophthalmology. However, major obstacles remain 28 in the generalization of these CNN's onto real-world DR screening programs. We believe these 29 difficulties are due to use of 1) small training datasets (<5,000 images), 2) private and 'curated' 30 repositories, 3) offline CNN implementation methods, while 4) relying on accuracy measured 31 as area under the curve (AUC) as the sole measure of CNN performance. 32 To address these issues, the public EyePACS Kaggle Diabetic Retinopathy dataset was 33 uploaded onto Microsoft Azure™ cloud platform. Two CNNs were trained as a "Quality 34 Assurance", and a "Classifier". The "Classifier" CNN performance was then tested both on 35 'un-curated' as well as the 'curated' test set created by the "Quality Assessment" CNN. Finally, 36 the sensitivity of the "Classifier" CNNs was boosted post-training using two post-training 37 techniques. 38Our "Classifier" CNN proved to be robust, as its performance was similar on 'curated' and 'un-39 curated' sets. The implementation of 'cascading thresholds' and 'max margin' techniques led 40 to significant improvements in the "Classifier" CNN's sensitivity, while also enhancing the 41 specificity of other grades. 42 43 4 44 45It is estimated that by 2040, nearly 600 million people will have diabetes worldwide(1). 46Diabetic retinopathy (DR) is a common diabetes-related microvascular complication, and is 47 the leading cause of preventable blindness in people of working age worldwide(2, 3). It has 48 been estimated that the overall prevalence of non-vision-threatening DR, vision-threatening 49 DR and the blinding diabetic eye disease were 34·6%, 10·2%, and 6·8% respectively (3-6). 50Clinical trials have shown that the risk of DR progression can be significantly reduced by 51 controlling major risk factors such as hyperglycaemia and hypertension (7-9). It is further 52 estimated that screening, appropriate referral and treatment can reduce the vision loss from DR 53 by 50% (10-12). However, DR screening programs are expensive to set up and administrate. It 54 is estimated that even in developed countries, these programs do not reach up to 30% of the 55 diabetic population (13, 14). 56 Artificial intelligence (AI) and its subcategory of deep learning have gained popularity in 57 medical screening programs, including DR screening. In deep learning, a convolutional neural 58 network (CNN) is designed and trained based on large datasets of ground truth data and labels. 59The CNN algorithm adjusts its weights and discovers which features to extract from medical 60 data (e.g. fundus photos) to achieve the best classification accuracy, when compared to human 61 performance (15-20). CNNs use layers with convolutions, which are defined as mathematical 62 functions that use filters to extract features from an image (21-23). The out...

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Section: Methodsologymentioning

confidence: 85%

Towards implementation of AI in New Zealand national screening program: Cloud-based, Robust, and Bespoke

Xie

Yang

Squirrell

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The choice of image size was to minimize the computational load on the Microsoft Azure™ platform, while not compromising the performance of the trained CNNs. According to existing literature [42] and based on our experience, larger image sizes would have led to diminishing returns in accuracy and overall performance of the designed CNNs. The resized cropped images were enhanced by applying a Gaussian blur technique [43], using the equation below.…”

Section: Pre-processingmentioning

confidence: 87%

Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke

et al. 2020

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Convolutional Neural Networks (CNNs) have become a prominent method of AI implementation in medical classification tasks. Grading Diabetic Retinopathy (DR) has been at the forefront of the development of AI for ophthalmology. However, major obstacles remain in the generalization of these CNNs onto real-world DR screening programs. We believe these difficulties are due to use of 1) small training datasets (<5,000 images), 2) private and 'curated' repositories, 3) locally implemented CNN implementation methods, while 4) relying on measured Area Under the Curve (AUC) as the sole measure of CNN performance. To address these issues, the public EyePACS Kaggle Diabetic Retinopathy dataset was uploaded onto Microsoft Azure™ cloud platform. Two CNNs were trained; 1 a "Quality Assurance", and 2. a "Classifier". The Diabetic Retinopathy classifier CNN (DRCNN) performance was then tested both on 'un-curated' as well as the 'curated' test set created by the "Quality Assessment" CNN model. Finally, the sensitivity of the DRCNNs was boosted using two post-training techniques. Our DRCNN proved to be robust, as its performance was similar on 'curated' and 'un-curated' test sets. The implementation of 'cascading thresholds' and 'max margin' techniques led to significant improvements in the DRCNN's sensitivity, while also enhancing the specificity of other grades. OPEN ACCESSCitation: Xie L, Yang S, Squirrell D, Vaghefi E (2020) Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke. PLoS ONE 15 (4): e0225015. https://doi.org/10.

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“…The study in [13] developed a deep learning system for identification of diabetic retinopathy with enhanced accuracy than existing studies. The analysis was performed on a small percentage of images with higher resolutions.…”

Section: Related Workmentioning

confidence: 99%

Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

et al. 2020

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Diabetic Retinopathy is a major cause of vision loss and blindness affecting millions of people across the globe. Although there are established screening methods - fluorescein angiography and optical coherence tomography for detection of the disease but in majority of the cases, the patients remain ignorant and fail to undertake such tests at an appropriate time. The early detection of the disease plays an extremely important role in preventing vision loss which is the consequence of diabetes mellitus remaining untreated among patients for a prolonged time period. Various machine learning and deep learning approaches have been implemented on diabetic retinopathy dataset for classification and prediction of the disease but majority of them have neglected the aspect of data pre-processing and dimensionality reduction, leading to biased results. The dataset used in the present study is a diabetes retinopathy dataset collected from the UCI machine learning repository. At its inceptions, the raw dataset is normalized using the Standardscalar technique and then Principal Component Analysis (PCA) is used to extract the most significant features in the dataset. Further, Firefly algorithm is implemented for dimensionality reduction. This reduced dataset is fed into a Deep Neural Network Model for classification. The results generated from the model is evaluated against the prevalent machine learning models and the results justify the superiority of the proposed model in terms of Accuracy, Precision, Recall, Sensitivity and Specificity.

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Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading

Cited by 196 publications

References 12 publications

Towards implementation of AI in New Zealand national screening program: Cloud-based, Robust, and Bespoke

Towards implementation of AI in New Zealand national screening program: Cloud-based, Robust, and Bespoke

Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke

Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

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