Classification for diabetic retinopathy by using staged convolutional neural network

Wang, Hongqiu; Sun, Yabing; Cao, Yunjian; Ouyang, Ganlu; Wang, Xin; Wu, Shaoen; Miao, Tian

doi:10.1109/cacml55074.2022.00045

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…IDRiD and Messidor dataset have been used to evaluate the proposed algorithms, yielding an accuracy of 92.6% and 92.9%, respectively. Wang et al [ 25 ] proposed a cascaded 2-Phase framework. The first module of the proposed framework classifies an input fundus image as diseased or normal based on deep features.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image Dataset

et al. 2022

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Diabetic Retinopathy affects one-third of all diabetic patients and may cause vision impairment. It has four stages of progression, i.e., mild non-proliferative, moderate non-proliferative, severe non-proliferative and proliferative Diabetic Retinopathy. The disease has no noticeable symptoms at early stages and may lead to chronic destruction, thus causing permanent blindness if not detected at an early stage. The proposed research provides deep learning frameworks for autonomous detection of Diabetic Retinopathy at an early stage using fundus images. The first framework consists of cascaded neural networks, spanned in three layers where each layer classifies data into two classes, one is the desired stage and the other output is passed to another classifier until the input image is classified as one of the stages. The second framework takes normalized, HSV and RGB fundus images as input to three Convolutional Neural Networks, and the resultant probabilistic vectors are averaged together to obtain the final output of the input image. Third framework used the Long Short Term Memory Module in CNN to emphasize the network in remembering information over a long time span. Proposed frameworks were tested and compared on the large-scale Kaggle fundus image dataset EYEPAC. The evaluations have shown that the second framework outperformed others and achieved an accuracy of 78.06% and 83.78% without and with augmentation, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image Dataset

et al. 2022

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“…Recently, DL method has achieved remarkable advancements in the domain of medical image, such as image classification [10] , detection [11] , and tumor segmentation [12] . CNN (convolutional neural network) architectures have found widespread application in the field of medical image processing, catering to diverse tasks and objectives.…”

Section: Introductionmentioning

confidence: 99%

Fine-grained attention & knowledge-based collaborative network for diabetic retinopathy grading

Tian

Wang

Sun

et al. 2023

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Empirical Analysis of Learnable Image Resizer for Large-Scale Medical Image Classification and Segmentation

Rahman,

Alom,

Khan

et al. 2024

NAECON 2024 - IEEE National Aerospace and Electronics Conference

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Classification for diabetic retinopathy by using staged convolutional neural network

Cited by 3 publications

References 18 publications

A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image Dataset

A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image Dataset

Fine-grained attention & knowledge-based collaborative network for diabetic retinopathy grading

Empirical Analysis of Learnable Image Resizer for Large-Scale Medical Image Classification and Segmentation

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