Diabetic Retinopathy Stage Classification Using Convolutional Neural Networks

Wang, Xiaoliang; Lu, Yue; Wang, Yujuan; Chen, Wei-Bang

doi:10.1109/iri.2018.00074

Cited by 131 publications

(72 citation statements)

References 15 publications

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“…Wang et al [59] investigated transfer learning techniques by using three network architectures: AlexNet, VGG16, and InceptionNetV3. The authors used 166 images from the Kaggle dataset to tune the algorithms.…”

Section: Paper Reviewmentioning

confidence: 99%

“…In studies [55,56,58,59,[61][62][63][68][69][70], the authors compared different architectures to determine the best performing one. In studies [56,59,61,63,70], the authors compared InceptionV3 architecture to other networks, and InceptionV3 achieved the best performance in all the studies except for [63]. The lowest performance was achieved by the AlexNet architecture in the following studies: [58,59,[61][62][63].…”

Section: Architectures Usedmentioning

confidence: 99%

“…In studies [56,59,61,63,70], the authors compared InceptionV3 architecture to other networks, and InceptionV3 achieved the best performance in all the studies except for [63]. The lowest performance was achieved by the AlexNet architecture in the following studies: [58,59,[61][62][63]. The high performance of InceptionV3 may be attributed to the inception module used.…”

Section: Architectures Usedmentioning

confidence: 99%

“…The optimizer can have a huge impact on the convergence of the training process, especially for transfer learning, as pointed out by Mohammadian et al [56] and Lam et al [62]. Four optimizers were mainly reported by the authors, namely SGD for studies [52][53][54][55][56][57][58][59][60][61][62][63][64][65] SGD with momentum for studies [56,58,59], Adam for studies [56,67,70], and RMSProp in [68]. The stochastic gradient descent optimizer (SGD) allows for a faster training process than the traditional gradient descent because it only considers, at each iteration, a subset of the training set.…”

Section: The Optimizers Usedmentioning

confidence: 99%

“…The LR chosen by the authors was very low to avoid losing the original weights of the layers, and it ranged from 1 × 10 −× to 1 × 10 −0 . Wang et al [59] used LR = 1 × 10 −0 for AlexNet and VGG16, as well as LR=1 × 10 −× for InceptionV3. Tsighe et al [65] used LR = 5 × 10 −0 .…”

Section: The Optimizers Usedmentioning

confidence: 99%

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Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

2020

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Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.

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Section: Paper Reviewmentioning

confidence: 99%

Section: Architectures Usedmentioning

confidence: 99%

Section: Architectures Usedmentioning

confidence: 99%

Section: The Optimizers Usedmentioning

confidence: 99%

Section: The Optimizers Usedmentioning

confidence: 99%

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Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

2020

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UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images

Kaur

2022

Concurrency and Computation

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Segmentation of diabetic retinopathy is a significant requirement for correct severity estimation level. Due to dissimilarity in shapes, size, and quantity of retinal lesions, the procedure of labor-intensive type of grading becomes a more time-consuming and challenging task. It imposes the prerequisite for an automated segmentation framework that can accurately identify the regions with margins and aid ophthalmologists in the early detection and monitoring of the severity level. The proposed work introduces a customized U-Net model based on the InceptionV3 pre-trained CNN network. It uses a periodic shuffling approach along with sub-pixel convolution initialized to convolution nearest-neighbor resize. The model performed training and validation for non-proliferative diabetic retinopathy that is, microaneurysms, hemorrhages, hard and soft exudates on two standard datasets such as IDRiD and DIARETDB1. The proposed model achieves 99.82% accuracy, 88.39% sensitivity, and 99.94% specificity for hemorrhages, 99.95% accuracy, 70.76% sensitivity, and 99.98% specificity for microaneurysms, 99.83% accuracy, 85.9% sensitivity, and 99.95% specificity for hard exudates, along with 99.62% accuracy, 85.38% sensitivity, and 99.71% specificity for soft exudates segmentation on the IDRID dataset. In comparison with both datasets, the proposed system on the IDRID dataset performed accurately with superior state-of-the-art outcomes for segmentation of the retinal lesions.

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Enhanced multi‐key privacy‐preserving distributed deep learning protocol with application to diabetic retinopathy diagnosis

Antwi‐Boasiako,

Zhou,

Liao

et al. 2024

Concurrency and Computation

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SummaryIn this work, privacy‐preserving distributed deep learning (PPDDL) is re‐visited with a specific application to diagnosing long‐term illness like diabetic retinopathy. In order to protect the privacy of participants datasets, a multi‐key PPDDL solution is proposed which is robust against collusion attacks and is also post‐quantum robust. Additionally, the PPDDL solution provides robust network security in terms of integrity of transmitted ciphertexts and keys, forward secrecy, and prevention of man‐in‐the‐middle attacks and is extensively verified using Verifpal. Proposed solution is evaluated on retina image datasets to detect diabetic retinopathy, with deep learning accuracy results of 96.30%, 96.21% and 96.20% for DDL, DDL + SINGLE and DDL + MULTI scenarios respectively. Results from our simulation indicate that accuracy of the PPDDL is maintained while protecting the privacy of the datasets of participants. Our proposed solution is also efficient in terms of the communication and run‐time costs.

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Diabetic Retinopathy Stage Classification Using Convolutional Neural Networks

Cited by 131 publications

References 15 publications

Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

UNIConv: An enhanced U‐Net based InceptionV3 convolutional model for DR semantic segmentation in retinal fundus images

Enhanced multi‐key privacy‐preserving distributed deep learning protocol with application to diabetic retinopathy diagnosis

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