FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2971731330> ?p ?o ?g. }

• W2971731330 abstract "Abstract Currently, Diabetes and the associated Diabetic Retinopathy (DR) instances are increasing at an alarming rate. Numerous previous research has focused on automated DR detection from fundus photography. The classification of severe cases of pathological indications in the eye has achieved over 90% accuracy. Still, the mild cases are challenging to detect due to CNN inability to identify the subtle features, discrimnative of disease. The data used (i.e. annotated fundus photographies) was obtained from 2 publicly available sources – Messidor and Kaggle. The experiments were conducted with 13 Convolutional Neural Networks architectures, pre-trained on large-scale ImageNet database using the concept of Transfer Learning. Several performance improvement techniques were applied, such as: (i) fine-tuning, (ii) data augmentation, and (iii) volume increase. The results were measured against the standard Accuracy metric on the testing dataset. After the extensive experimentation, the maximum Accuracy of 86% on No DR/Mild DR classification task was obtained for ResNet50 model with fine-tuning (un-freeze and re-train the layers from 100 onwards), and RMSProp Optimiser trained on the combined Messidor + Kaggle (aug) datasets. Despite promising results, Deep learning continues to be an empirical approach that requires extensive experimentation in order to arrive at the most optimal solution. The comprehensive evaluation of numerous CNN architectures was conducted in order to facilitate an early DR detection. Furthermore, several performance improvement techniques were assessed to address the CNN limitation in subtle eye lesions identification. The model also included various levels of image quality (low/high resolution, under/over-exposure, out-of-focus etc.), in order to prove its robustness and ability to adapt to real-world conditions." @default.
• W2971731330 created "2019-09-12" @default.
• W2971731330 creator A5030661817 @default.
• W2971731330 creator A5049750015 @default.
• W2971731330 creator A5053725057 @default.
• W2971731330 creator A5055060253 @default.
• W2971731330 creator A5086577635 @default.
• W2971731330 date "2019-09-09" @default.
• W2971731330 modified "2023-10-17" @default.
• W2971731330 title "Convolutional neural networks for mild diabetic retinopathy detection: an experimental study" @default.
• W2971731330 cites W1975032911 @default.
• W2971731330 cites W1989786678 @default.
• W2971731330 cites W2056526409 @default.
• W2971731330 cites W2103111028 @default.
• W2971731330 cites W2142866653 @default.
• W2971731330 cites W2161381512 @default.
• W2971731330 cites W2183341477 @default.
• W2971731330 cites W2194775991 @default.
• W2971731330 cites W2268996142 @default.
• W2971731330 cites W2502390809 @default.
• W2971731330 cites W2529609428 @default.
• W2971731330 cites W2531409750 @default.
• W2971731330 cites W2558381168 @default.
• W2971731330 cites W2752747624 @default.
• W2971731330 cites W2772246530 @default.
• W2971731330 cites W2894558420 @default.
• W2971731330 cites W2934420704 @default.
• W2971731330 cites W2963163009 @default.
• W2971731330 cites W2963446712 @default.
• W2971731330 cites W2964081807 @default.
• W2971731330 cites W2964350391 @default.
• W2971731330 doi "https://doi.org/10.1101/763136" @default.
• W2971731330 hasPublicationYear "2019" @default.
• W2971731330 type Work @default.
• W2971731330 sameAs 2971731330 @default.
• W2971731330 citedByCount "17" @default.
• W2971731330 countsByYear W29717313302020 @default.
• W2971731330 countsByYear W29717313302021 @default.
• W2971731330 countsByYear W29717313302022 @default.
• W2971731330 countsByYear W29717313302023 @default.
• W2971731330 crossrefType "posted-content" @default.
• W2971731330 hasAuthorship W2971731330A5030661817 @default.
• W2971731330 hasAuthorship W2971731330A5049750015 @default.
• W2971731330 hasAuthorship W2971731330A5053725057 @default.
• W2971731330 hasAuthorship W2971731330A5055060253 @default.
• W2971731330 hasAuthorship W2971731330A5086577635 @default.
• W2971731330 hasBestOaLocation W29717313301 @default.
• W2971731330 hasConcept C108583219 @default.
• W2971731330 hasConcept C118487528 @default.
• W2971731330 hasConcept C119857082 @default.
• W2971731330 hasConcept C134018914 @default.
• W2971731330 hasConcept C150899416 @default.
• W2971731330 hasConcept C153180895 @default.
• W2971731330 hasConcept C154945302 @default.
• W2971731330 hasConcept C162324750 @default.
• W2971731330 hasConcept C176217482 @default.
• W2971731330 hasConcept C21547014 @default.
• W2971731330 hasConcept C2776391266 @default.
• W2971731330 hasConcept C2776474195 @default.
• W2971731330 hasConcept C2778257484 @default.
• W2971731330 hasConcept C2779829184 @default.
• W2971731330 hasConcept C2780248432 @default.
• W2971731330 hasConcept C41008148 @default.
• W2971731330 hasConcept C555293320 @default.
• W2971731330 hasConcept C71924100 @default.
• W2971731330 hasConcept C81363708 @default.
• W2971731330 hasConceptScore W2971731330C108583219 @default.
• W2971731330 hasConceptScore W2971731330C118487528 @default.
• W2971731330 hasConceptScore W2971731330C119857082 @default.
• W2971731330 hasConceptScore W2971731330C134018914 @default.
• W2971731330 hasConceptScore W2971731330C150899416 @default.
• W2971731330 hasConceptScore W2971731330C153180895 @default.
• W2971731330 hasConceptScore W2971731330C154945302 @default.
• W2971731330 hasConceptScore W2971731330C162324750 @default.
• W2971731330 hasConceptScore W2971731330C176217482 @default.
• W2971731330 hasConceptScore W2971731330C21547014 @default.
• W2971731330 hasConceptScore W2971731330C2776391266 @default.
• W2971731330 hasConceptScore W2971731330C2776474195 @default.
• W2971731330 hasConceptScore W2971731330C2778257484 @default.
• W2971731330 hasConceptScore W2971731330C2779829184 @default.
• W2971731330 hasConceptScore W2971731330C2780248432 @default.
• W2971731330 hasConceptScore W2971731330C41008148 @default.
• W2971731330 hasConceptScore W2971731330C555293320 @default.
• W2971731330 hasConceptScore W2971731330C71924100 @default.
• W2971731330 hasConceptScore W2971731330C81363708 @default.
• W2971731330 hasLocation W29717313301 @default.
• W2971731330 hasOpenAccess W2971731330 @default.
• W2971731330 hasPrimaryLocation W29717313301 @default.
• W2971731330 hasRelatedWork W3018421652 @default.
• W2971731330 hasRelatedWork W3021430260 @default.
• W2971731330 hasRelatedWork W3091976719 @default.
• W2971731330 hasRelatedWork W3192840557 @default.
• W2971731330 hasRelatedWork W4220996320 @default.
• W2971731330 hasRelatedWork W4285149559 @default.
• W2971731330 hasRelatedWork W4285153837 @default.
• W2971731330 hasRelatedWork W4310880831 @default.
• W2971731330 hasRelatedWork W4312200629 @default.
• W2971731330 hasRelatedWork W4382286161 @default.
• W2971731330 isParatext "false" @default.
• W2971731330 isRetracted "false" @default.

• next