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https://dspace.univ-ouargla.dz/jspui/handle/123456789/34714
Title: | Diabetic Retinopathy Detection Using Deep Learning Techniques |
Authors: | AIADI, Oussama MOUALDI, Abdessalam |
Keywords: | Artificial Intelligence (AI) Healthcare Diabetic Retinopathy (DR) Deep learning techniques Early detection Denoising Convolutional Autoencoder (DCAE) Contrastive Predictive Coding (CPC) Transformers Pre-processing techniques Attention mechanisms Classification |
Issue Date: | 2023 |
Publisher: | UNIVERSITY OF KASDI MERBAH OUARGLA |
Abstract: | DiabeticRetinopathy(DR)diagnosisholdsimmensepotentialfortransformationthrough theapplicationofArtificialIntelligence(AI)inhealthcare. Thisstudyfocusesonutilizing deeplearningtechniquestoaddressthelimitationsofcurrentdiagnosticmethodsforDR. Byanalyzingretinalimages,AIcanaccuratelydetectearlysignsofDR,leadingtotimely interventionsandbetterpatientoutcomes. Thethesisalsoaddressesthechallengeoflowresolution DR images obtained from handheld fundus cameras. To enhance the quality of DR images, a comprehensive approach is adopted, integrating various deep-learning techniques. The Denoising Convolutional Autoencoder (DCAE) is used to reduce noise and artifacts in the images. Advanced models like Contrastive PredictiveCoding(CPC)andVisionTransformers,alongwithpre-processingtechniques, are employed for accurate classification. The comparative analysis shows that attention mechanisms,particularlyintheCPCandTransformersmodels,significantlyimproveDR classification accuracy. Compared to other models, CPC and Transformers outperform in terms of accuracy, sensitivity,andspecificity. Attentionmechanismsenablethesemodelstocapturerelevant features and focus on critical areas within the images. The research findings highlight the potential of AI-based systems, particularly attention-based models, in improving DR diagnosis. Implementing these advancements can lead to better patient outcomes, reduced healthcare costs, and prevention of DR progression to severe stages. |
URI: | https://dspace.univ-ouargla.dz/jspui/handle/123456789/34714 |
Appears in Collections: | Département d'Electronique et des Télécommunications - Master |
Files in This Item:
File | Description | Size | Format | |
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MOUALDI.pdf | 15,23 MB | Adobe PDF | View/Open |
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