Diabetic Macular Edema Grading Based on Deep Neural Networks

Al-Bander, Baidaa; Al-Nuaimy, Waleed; Al-Taee, Majid A.; Williams, Bryan; Zheng, Yalin

doi:10.17077/omia.1055

Cited by 33 publications

(19 citation statements)

References 15 publications

(23 reference statements)

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“…Thus deep learning emerged as a more promising approach to learn features automatically. One of the most recent works [19] used CNNs to automatically extract features and grade the input fundus images. The authors obtained an accuracy of 88.8%, sensitivity of 74.7% and specificity of 96.5% on MESSIDOR dataset [19].…”

Section: Related Workmentioning

confidence: 99%

DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs

Singh

Gorantla

2020

PLoS ONE

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Diabetic Macular Edema (DME) is an advanced stage of Diabetic Retinopathy (DR) and can lead to permanent vision loss. Currently, it affects 26.7 million people globally and on account of such a huge number of DME cases and the limited number of ophthalmologists, it is desirable to automate the diagnosis process. Computer-assisted, deep learning based diagnosis could help in early detection, following which precision medication can help to mitigate the vision loss. Method: In order to automate the screening of DME, we propose a novel DMENet Algorithm which is built on the pillars of Convolutional Neural Networks (CNNs). DMENet analyses the preprocessed color fundus images and passes it through a two-stage pipeline. The first stage detects the presence or absence of DME whereas the second stage takes only the positive cases and grades the images based on severity. In both the stages, we use a novel Hierarchical Ensemble of CNNs (HE-CNN). This paper uses two of the popular publicly available datasets IDRiD and MESSIDOR for classification. Preprocessing on the images is performed using morphological opening and gaussian kernel. The dataset is augmented to solve the class imbalance problem for better performance of the proposed model. Results: The proposed methodology achieved an average Accuracy of 96.12%, Sensitivity of 96.32%, Specificity of 95.84%, and F−1 score of 0.9609 on MESSI-DOR and IDRiD datasets. Conclusion: These excellent results establish the validity of the proposed methodology for use in DME screening and solidifies the applicability of the HE-CNN classification technique in the domain of biomedical imaging.

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Section: Related Workmentioning

confidence: 99%

DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs

Singh

Gorantla

2020

PLoS ONE

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“…Diabetic Macular Edema Al-Bander et al [67] proposed a CNN system to grade the severity of DME using fundus images using the MESSIDOR [77] dataset of 1200images. They obtained an Acc of 88.8%, sensitivity of 74.7% and specificity of 96.5% respectively.…”

Section: B DL Approaches Employing New Networkmentioning

confidence: 99%

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

et al. 2020

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Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This paper presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes.

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“…The geometrical shape and orientation features of exudates are considered for accurately segmenting exudates resulting in an overall accuracy of 91% for 189 fundus images from DIARETDB1 and MESSIDOR databases. ME grading based on feature learning approach and deep neural networks was proposed by B. Al-Bander et al [3]. The algorithm was tested on MESSIDOR database and an accuracy of 88.8% was obtained using this method.…”

Section: Literature Reviewmentioning

confidence: 99%

Severity Analysis of Macular Edema using Random Tree Classifier

Prasad¹,

Vibha²,

Venugopal

2018

int. jour. eng. com. sci

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Abstract:Macular edema ensues when there is abnormal pile-up of fluid and results in swelling of the macula part of the retina. It is commonly associated with diabetes. It can be diagnosed by identifying exudates in the retinal images. In the proposed work, macular the retinal image is pre-processed, enhanced and segmented using morphological operations. The optic disc and macula are segmented. Various statistical features are extracted. Optimal features are selected using Haar wavelets. The selected features are classified using Random tree classifier to detect the severity of the disease in to three stages namely, normal, mild and critical. The accuracy obtained is 98.4%.

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Diabetic Macular Edema Grading Based on Deep Neural Networks

Cited by 33 publications

References 15 publications

DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs

DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

Severity Analysis of Macular Edema using Random Tree Classifier

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