Automated identification and grading system of diabetic retinopathy using deep neural networks

Zhang, Wei; Zhong, Jie; Yang, Sha; Gao, Zhan; Hu, Junjie; Chen, Yuanyuan; Zhang, Yi

doi:10.1016/j.knosys.2019.03.016

Cited by 212 publications

(121 citation statements)

References 28 publications

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“…Zhang et al [68] used a private dataset with 13,767 images to propose a model called DeepDR, which uses deep learning based on transfer learning models to detect DR. The model consists of three stages: identification, grading, and reporting.…”

Section: Paper Reviewmentioning

confidence: 99%

“…Table 3 shows the list of the reviewed papers that applied transfer learning to classify DR. * The results from Li et al [55] are the results of fine-tuning the entire networks by using the Messidor dataset. The results from Zhang et al [68] are the results of the grading model. The results shown from Lam et al [62] are the results of GoogLeNet architecture for the two-class Kaggle dataset.…”

Section: Paper Reviewmentioning

confidence: 99%

“…The choice of the architectures did not depend on the size of the dataset. 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].…”

Section: Architectures 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: Paper Reviewmentioning

confidence: 99%

Section: Architectures 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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“…It is evident that deep learning is a powerful tool in today's artificial intelligence-based tasks. Recently, one deep learning method, i.e., convolutional neural network (CNN), has produced impressive results in biomedical imaging and CAD systems, e.g., cancer, brain tumor, and retinopathy detection [139,140]. Since DR is a life-threatening disease and requires early diagnosis to control its prevalence in patients, computerized tools have proven to be effective in early DR assessment, however, a gap remains regarding fast and real time solutions for DR prediction.…”

Section: Latest Trendsmentioning

confidence: 99%

“…Each neuron outcome is then mixed to maintain overlapping among input areas to better represent the original image information. This procedure is pursued for all layers until desirable results are achieved [135][136][137][138][139][140][141][142]. [145] CNN model Detection of exudates -- [113] Multiscale and CNN Detection of fovea and OD -AC: 97%…”

Section: Latest Trendsmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

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Modeling of Diabetic Retinopathy Grading Using Deep Learning

Srinivasan¹,

Rajalingam²,

Arokiachamy³

2021

Simulation and Analysis of Mathematical Methods in Real‐Time Engineering Applications

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Automated identification and grading system of diabetic retinopathy using deep neural networks

Cited by 212 publications

References 28 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

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Modeling of Diabetic Retinopathy Grading Using Deep Learning

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