Deep Learning–Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model.
The diabetic retinopathy accounts in the deterioration of retinal blood vessels leading to a serious compilation affecting the eyes. The automated DR diagnosis frameworks are critically important for the early identification and detection of these eye-related problems, helping the ophthalmic experts...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 2; pp. 440 - 458 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2021
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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=151472669&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151472669 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2021 vid: 34 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151472669 149119445 151472669 151472669 10.1007/s10278-021-00418-5 151472669 ppf: 440 ppct: 18 formats: fmt: @attributes: type: P tig: atl: Deep Learning–Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model. aug: au: Bhardwaj, Charu Jain, Shruti Sood, Meenakshi affil: Department of Electronics and Communication Engineering, JUIT Waknaghat, Solan, HP, India sug: subj: Deep Learning Methods Diabetic Retinopathy Diagnosis Severity of Illness Models, Theoretical Human ab: The diabetic retinopathy accounts in the deterioration of retinal blood vessels leading to a serious compilation affecting the eyes. The automated DR diagnosis frameworks are critically important for the early identification and detection of these eye-related problems, helping the ophthalmic experts in providing the second opinion for effectual treatment. The deep learning techniques have evolved as an improvement over the conventional approaches, which are dependent on the handcrafted feature extraction. To address the issue of proficient DR discrimination, the authors have proposed a quadrant ensemble automated DR grading approach by implementing InceptionResnet-V2 deep neural network framework. The presented model incorporates histogram equalization, optical disc localization, and quadrant cropping along with the data augmentation step for improving the network performance. A superior accuracy performance of 93.33% is observed for the proposed framework, and a significant reduction of 0.325 is noticed in the cross-entropy loss function for MESSIDOR benchmark dataset; however, its validation utilizing the latest IDRiD dataset establishes its generalization ability. The accuracy improvement of 13.58% is observed when the proposed QEIRV-2 model is compared with the classical Inception-V3 CNN model. To justify the viability of the proposed framework, its performance is compared with the existing state-of-the-art approaches and 25.23% of accuracy improvement is observed. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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