FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4327662933> ?p ?o ?g. }

• W4327662933 endingPage "27601" @default.
• W4327662933 startingPage "27590" @default.
• W4327662933 abstract "Recently, the Internet of Things (IoT) and computer vision technologies find useful in different applications, especially in healthcare. IoT driven healthcare solutions provide intelligent solutions for enabling substantial reduction of expenses and improvisation of healthcare service quality. At the same time, Diabetic Retinopathy (DR) can be described as permanent blindness and eyesight damage because of the diabetic condition in humans. Accurate and early detection of DR could decrease the loss of damage. Computer-Aided Diagnoses (CAD) model based on retinal fundus image is a powerful tool to help experts diagnose DR. Some traditional Machine Learning (ML) based DR diagnoses model has currently existed in this study. The recent developments of Deep Learning (DL) and its considerable achievement over conventional ML algorithms for different applications make it easier to design effectual DR diagnosis model. With this motivation, this paper presents a novel IoT and DL enabled diabetic retinopathy diagnosis model (IoTDL-DRD) using retinal fundus images. The presented Internet of Things Deep Learning – Diabetic Retinopathy Diagnosis (IoTDL-DRD) technique utilizes IoT devices for data collection purposes and then transfers them to the cloud server to process them. Followed by, the retinal fundus images are preprocessed to remove noise and improve contrast level. Next, mayfly optimization based region growing (MFORG) based segmentation technique is utilized to detect lesion regions in the fundus image. Moreover, densely connected network (DenseNet) based feature extractor and Long Short Term Memory (LSTM) based classifier is used for effective DR diagnosis. Furthermore, the parameter optimization of the LSTM method can be carried out by Honey Bee Optimization (HBO) algorithm. For evaluating the improved DR diagnostic outcomes of the IoTDL-DRD technique, a comprehensive set of simulations were carried out. A wide ranging comparison study reported the superior performance of the proposed method." @default.
• W4327662933 created "2023-03-18" @default.
• W4327662933 creator A5065953815 @default.
• W4327662933 creator A5069411925 @default.
• W4327662933 date "2023-01-01" @default.
• W4327662933 modified "2023-09-26" @default.
• W4327662933 title "Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images" @default.
• W4327662933 cites W1969496006 @default.
• W4327662933 cites W2605687850 @default.
• W4327662933 cites W2884680377 @default.
• W4327662933 cites W2896784509 @default.
• W4327662933 cites W2992400503 @default.
• W4327662933 cites W2996262564 @default.
• W4327662933 cites W3004707432 @default.
• W4327662933 cites W3080758677 @default.
• W4327662933 cites W3100548141 @default.
• W4327662933 cites W3107007349 @default.
• W4327662933 cites W3127078435 @default.
• W4327662933 cites W3133876396 @default.
• W4327662933 cites W3155103916 @default.
• W4327662933 cites W3173654020 @default.
• W4327662933 cites W3193298149 @default.
• W4327662933 cites W3205018926 @default.
• W4327662933 cites W3211833946 @default.
• W4327662933 cites W4206098167 @default.
• W4327662933 cites W4212945932 @default.
• W4327662933 cites W4220792848 @default.
• W4327662933 cites W4281260702 @default.
• W4327662933 cites W4281678046 @default.
• W4327662933 cites W4283264624 @default.
• W4327662933 cites W4283376326 @default.
• W4327662933 cites W4283768393 @default.
• W4327662933 cites W4285093832 @default.
• W4327662933 cites W4306790045 @default.
• W4327662933 doi "https://doi.org/10.1109/access.2023.3257988" @default.
• W4327662933 hasPublicationYear "2023" @default.
• W4327662933 type Work @default.
• W4327662933 citedByCount "0" @default.
• W4327662933 crossrefType "journal-article" @default.
• W4327662933 hasAuthorship W4327662933A5065953815 @default.
• W4327662933 hasAuthorship W4327662933A5069411925 @default.
• W4327662933 hasBestOaLocation W43276629331 @default.
• W4327662933 hasConcept C108583219 @default.
• W4327662933 hasConcept C118487528 @default.
• W4327662933 hasConcept C119857082 @default.
• W4327662933 hasConcept C124504099 @default.
• W4327662933 hasConcept C134018914 @default.
• W4327662933 hasConcept C142724271 @default.
• W4327662933 hasConcept C154945302 @default.
• W4327662933 hasConcept C2776391266 @default.
• W4327662933 hasConcept C2776474195 @default.
• W4327662933 hasConcept C2779549770 @default.
• W4327662933 hasConcept C2779829184 @default.
• W4327662933 hasConcept C2780248432 @default.
• W4327662933 hasConcept C2780827179 @default.
• W4327662933 hasConcept C31972630 @default.
• W4327662933 hasConcept C41008148 @default.
• W4327662933 hasConcept C52622490 @default.
• W4327662933 hasConcept C534262118 @default.
• W4327662933 hasConcept C555293320 @default.
• W4327662933 hasConcept C71924100 @default.
• W4327662933 hasConcept C89600930 @default.
• W4327662933 hasConceptScore W4327662933C108583219 @default.
• W4327662933 hasConceptScore W4327662933C118487528 @default.
• W4327662933 hasConceptScore W4327662933C119857082 @default.
• W4327662933 hasConceptScore W4327662933C124504099 @default.
• W4327662933 hasConceptScore W4327662933C134018914 @default.
• W4327662933 hasConceptScore W4327662933C142724271 @default.
• W4327662933 hasConceptScore W4327662933C154945302 @default.
• W4327662933 hasConceptScore W4327662933C2776391266 @default.
• W4327662933 hasConceptScore W4327662933C2776474195 @default.
• W4327662933 hasConceptScore W4327662933C2779549770 @default.
• W4327662933 hasConceptScore W4327662933C2779829184 @default.
• W4327662933 hasConceptScore W4327662933C2780248432 @default.
• W4327662933 hasConceptScore W4327662933C2780827179 @default.
• W4327662933 hasConceptScore W4327662933C31972630 @default.
• W4327662933 hasConceptScore W4327662933C41008148 @default.
• W4327662933 hasConceptScore W4327662933C52622490 @default.
• W4327662933 hasConceptScore W4327662933C534262118 @default.
• W4327662933 hasConceptScore W4327662933C555293320 @default.
• W4327662933 hasConceptScore W4327662933C71924100 @default.
• W4327662933 hasConceptScore W4327662933C89600930 @default.
• W4327662933 hasFunder F4320322322 @default.
• W4327662933 hasLocation W43276629331 @default.
• W4327662933 hasOpenAccess W4327662933 @default.
• W4327662933 hasPrimaryLocation W43276629331 @default.
• W4327662933 hasRelatedWork W2005437358 @default.
• W4327662933 hasRelatedWork W2517104666 @default.
• W4327662933 hasRelatedWork W2790662084 @default.
• W4327662933 hasRelatedWork W2916155412 @default.
• W4327662933 hasRelatedWork W3137421597 @default.
• W4327662933 hasRelatedWork W4285153837 @default.
• W4327662933 hasRelatedWork W4285827401 @default.
• W4327662933 hasRelatedWork W4309637067 @default.
• W4327662933 hasRelatedWork W4310880831 @default.
• W4327662933 hasRelatedWork W4380075502 @default.
• W4327662933 hasVolume "11" @default.
• W4327662933 isParatext "false" @default.

• next