A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs.
Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings published in implementation and verification of a deep...
| Publicado en: | BioMed Research International pp. 1 - 16 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
6/7/2022
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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=157300590&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157300590 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 6/7/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 157300590 157300590 157300590 10.1155/2022/3163496 157300590 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs. aug: au: Gunasekaran, K. Pitchai, R. Chaitanya, Gogineni Krishna Selvaraj, D. Annie Sheryl, S. Almoallim, Hesham S. Alharbi, Sulaiman Ali Raghavan, S. S. Tesemma, Belachew Girma affil: Department of Computer Science and Engineering, Sri Indu College of Engineering and Technology, Hyderabad, Telangana 501510, India sug: subj: Diabetic Retinopathy Diagnosis Photography Methods Deep Learning Early Diagnosis Retina Analysis Neural Networks (Computer) Human Disease Progression Diabetic Patients Blood Vessels Subtraction Technique Descriptive Statistics Image Processing, Computer Assisted ab: Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings published in implementation and verification of a deep learning approach for diabetic retinopathy identification in retinal fundus pictures. To address this issue, the suggested investigative study uses recurrent neural networks (RNN) to retrieve characteristics from deep networks. As a result, using computational approaches to identify certain disorders automatically might be a fantastic solution. We developed and tested several iterations of a deep learning framework to forecast the progression of diabetic retinopathy in diabetic individuals who have undergone teleretinal diabetic retinopathy assessment in a basic healthcare environment. A collection of one-field or three-field colour fundus pictures served as the input for both iterations. Utilizing the proposed DRNN methodology, advanced identification of the diabetic state was performed utilizing HE detected in an eye's blood vessel. This research demonstrates the difficulties in duplicating deep learning approach findings, as well as the necessity for more reproduction and replication research to verify deep learning techniques, particularly in the field of healthcare picture processing. This development investigates the utilization of several other Deep Neural Network Frameworks on photographs from the dataset after they have been treated to suitable image computation methods such as local average colour subtraction to assist in highlighting the germane characteristics from a fundoscopy, thus, also enhancing the identification and assessment procedure of diabetic retinopathy and serving as a skilled guidelines framework for practitioners all over the globe. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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