Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning.
Retinopathy of prematurity (ROP) is a potentially blinding disorder seen in low birth weight preterm infants. In India, the burden of ROP is high, with nearly 200,000 premature infants at risk. Early detection through screening and treatment can prevent this blindness. The automatic screening system...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 4; pp. 932 - 948 |
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| Autores principales: | , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152559505&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152559505 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2021 vid: 34 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152559505 151300816 152559505 152559505 10.1007/s10278-021-00477-8 152559505 ppf: 932 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning. aug: au: Agrawal, Ranjana Kulkarni, Sucheta Walambe, Rahee Kotecha, Ketan affil: School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, India sug: subj: Retinopathy of Prematurity Pathology Retinopathy of Prematurity Diagnosis Deep Learning Diagnosis, Computer Assisted Automation Early Diagnosis Conceptual Framework Human Machine Learning Artificial Intelligence Digital Imaging Image Interpretation, Computer Assisted Image Processing, Computer Assisted Vision Screening Severity of Illness Systems Development Descriptive Statistics ab: Retinopathy of prematurity (ROP) is a potentially blinding disorder seen in low birth weight preterm infants. In India, the burden of ROP is high, with nearly 200,000 premature infants at risk. Early detection through screening and treatment can prevent this blindness. The automatic screening systems developed so far can detect "severe ROP" or "plus disease," but this information does not help schedule follow-up. Identifying vascularized retinal zones and detecting the ROP stage is essential for follow-up or discharge from screening. There is no automatic system to assist these crucial decisions to the best of the authors' knowledge. The low contrast of images, incompletely developed vessels, macular structure, and lack of public data sets are a few challenges in creating such a system. In this paper, a novel method using an ensemble of "U-Network" and "Circle Hough Transform" is developed to detect zones I, II, and III from retinal images in which macula is not developed. The model developed is generic and trained on mixed images of different sizes. It detects zones in images of variable sizes captured by two different imaging systems with an accuracy of 98%. All images of the test set (including the low-quality images) are considered. The time taken for training was only 14 min, and a single image was tested in 30 ms. The present study can help medical experts interpret retinal vascular status correctly and reduce subjective variation in diagnosis. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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