Detection of Glaucoma from Fundus Images Using Novel Evolutionary-Based Deep Neural Network.
Glaucoma is an asymptotic condition that damages the optic nerves of a human eye. Glaucoma is frequently caused due to abnormally high pressure in an eye that leads to permanent blindness. Detecting glaucoma at an initial phase has the possibility of curing this disease, but diagnosing accurately is...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 4; pp. 1008 - 1023 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2022
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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=159195610&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159195610 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2022 vid: 35 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159195610 155676154 159195610 159195610 10.1007/s10278-021-00577-5 159195610 ppf: 1008 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Detection of Glaucoma from Fundus Images Using Novel Evolutionary-Based Deep Neural Network. aug: au: Madhumalini, M. Devi, T. Meera affil: Department of Electronics and Communication Engineering, P. A. College of Engineering and Technology, Pollachi, Tamilnadu, India sug: subj: Glaucoma Diagnosis Retina Radiography Neural Networks (Computer) Diagnosis, Eye Methods Human Optic Nerve Experimental Studies Predictive Value of Tests Sensitivity and Specificity Tomography, Optical Coherence Radiographic Image Interpretation, Computer-Assisted Eye Blood Supply Radiographic Image Enhancement Models, Statistical ab: Glaucoma is an asymptotic condition that damages the optic nerves of a human eye. Glaucoma is frequently caused due to abnormally high pressure in an eye that leads to permanent blindness. Detecting glaucoma at an initial phase has the possibility of curing this disease, but diagnosing accurately is considered as a challenging task. Therefore, this paper proposes a novel method known as a glaucoma detection system that performs the diagnosis of glaucoma by exploiting the prescribed characteristics. The significant intention of this paper involves diagnosing the glaucoma disease present at the top optical nerve of a human eye. The proposed glaucoma detection has used four different phases namely data preprocessing or enhancement phase, segmentation phase, feature extraction phase, and classification phase. Here, a novel classifier named fractional gravitational search-based hybrid deep neural network (FGSA-HDNN) is developed for the effective classification of glaucoma-infected images from the normal image. Finally, the experimental analysis for the proposed approach and various other techniques are performed, and the accuracy rate while diagnosing glaucoma achieved is 98.75%. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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