Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning

Agrawal, Ranjana; Kulkarni, Sucheta; Walambe, Rahee; Kotecha, Ketan

doi:10.1007/s10278-021-00477-8

Cited by 26 publications

(22 citation statements)

References 39 publications

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“…ere are pixel accuracy (PA), mean pixel accuracy (MPA), and mean intersection over union (MIoU) selected as indicators to evaluate performance of method, and the calculation methods are as follows [19][20][21][22]:…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Research on Segmentation Method of Greening Landscape of Urban Community Based on Improved U-Net Network

Sui

2022

Security and Communication Networks

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Aiming at the problem of low segmentation accuracy of greening landscape of urban community, a segmentation method of greening landscape of urban community based on improved U-Net network is proposed by adding an encoder on U-Net network. In addition, simulation is carried out on the remote sensing image data collected by GPRS network, and the effectiveness of the method is verified. The simulation results show that the result of proposed method is close to that of manual annotation. Compared with the traditional segmentation algorithm SVM and SegNet method, as well as the U-Net network before improvement, the proposed method has higher segmentation accuracy. The segmentation accuracy can reach up to 91%, the intersection ratio can reach 76%, and the mean pixel accuracy and mean intersection ratio can reach 89% and 74%, which indicates that the proposed method has certain validity and practicability.

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Section: Evaluation Indicatorsmentioning

confidence: 99%

Research on Segmentation Method of Greening Landscape of Urban Community Based on Improved U-Net Network

Sui

2022

Security and Communication Networks

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“…To detect early ROP and staging, Huang et al (2021) constructed an ROP staging model using a through convolution neural network. They randomly divided To detect the blood vessels in areas I, II, and III of children with ROP and to assist in assessing the severity of ROP, Agrawal et al (2021) built an AI model by combining U-Net and Circle Hough Transform. They collected 4,250 fundus images to develop and test the AI model, all of which were labeled by ROP experts.…”

Section: Application Of Artificial Intelligence In Retinopathy Of Pre...mentioning

confidence: 99%

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Liu

et al. 2023

Front. Cell Dev. Biol.

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As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

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“…A twofold training approach has been adopted, where the model is trained with manually labeled images to generate images with bounding boxes (pseudo labeled images), then both manually labeled and pseudo labeled images are used for retraining the model which is finally used for predicting ROP. For establishing ROP diagnosis along with an assisted medical follow-up mechanism, Agrawal et al [168] developed a network that uses an ensemble of U-Net and hough transform techniques to detect various zones (I, II, III) in fundus images of infants (premature). These zones are used to indicate ROP severity and assist in scheduling the next screening.…”

Section: E Rop Diagnosismentioning

confidence: 99%

A Comprehensive Review of Deep Learning Strategies in Retinal Disease Diagnosis Using Fundus Images

et al. 2022

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In recent years, there has been an unprecedented growth in computer vision and deep learning implementation owing to the exponential rise of computation infrastructure. The same was also reflected in retinal image analysis and successful artificial intelligence models were developed for various retinal disease diagnoses using a wide variety of visual markers obtained from eye fundus images. This article presents a comprehensive study of different deep learning strategies employed in recent times for the diagnosis of five major eye diseases, i.e., Diabetic retinopathy, Glaucoma, age-related macular degeneration, Cataract, and Retinopathy of prematurity. This article is organized according to the deep learning implementation process pipeline, where commonly used datasets, evaluation metrics, image pre-processing techniques, and deep learning backbone models are first illustrated followed by an extensive review of different strategies for each of the five mentioned retinal diseases is presented. Finally, this article summarizes eight major research directions available in the field of retinal disease diagnosis and outlines key challenges and future scope for the present research community.

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Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning

Cited by 26 publications

References 39 publications

Research on Segmentation Method of Greening Landscape of Urban Community Based on Improved U-Net Network

Research on Segmentation Method of Greening Landscape of Urban Community Based on Improved U-Net Network

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

A Comprehensive Review of Deep Learning Strategies in Retinal Disease Diagnosis Using Fundus Images

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