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              <text>Deep Dive Into Diabetic Retinopathy Identification: A Deep Learning Approach with Blood Vessel Segmentation and Lesion Detection</text>
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              <text>Deep learning; diabetic retinopathy; hemorrhages; lesions; microaneurysms; soft exudates</text>
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              <text>In the landscape of diabetes-related ocular complications, diabetic retinopathy stands as a formidable challenge, reigning as the leading cause of vision impairment worldwide. Despite extensive research, the quest for effective treatments remains an ongoing pursuit. This study explores the burgeoning domain of AI-driven approaches in ocular research, particularly focusing on diabetic retinopathy detection. It delves into various diagnostic methodologies, encompassing the detection of microaneurysms, identification of hemorrhages, and segmentation of blood vessels, primarily utilizing retinal fundus photographs. Our findings juxtapose conventional machine learning techniques against deep neural networks, showcasing the remarkable efficacy of Convolutional neural network (CNN) and Random Forest (RF) in segmenting blood vessels and the robustness of deep learning in lesion identification. As we navigate the quest for clearer vision, artificial intelligence takes center stage, promising a transformative leap forward in the realm of vision care.  2024 River Publishers.</text>
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              <text>Upreti K.; Kapoor A.; Hundekari S.; Upreti S.; Kaul K.; Kapoor S.; Tiwari A.</text>
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              <text>Journal of Mobile Multimedia, Vol-20, No. 2, pp. 495-524.</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.13052/jmm1550-4646.20210" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.13052/jmm1550-4646.20210&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195080854&amp;amp;doi=10.13052%2Fjmm1550-4646.20210&amp;amp;partnerID=40&amp;amp;md5=3209980c2ad4abab645518b8af3a7de3" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195080854&amp;amp;doi=10.13052%2fjmm1550-4646.20210&amp;amp;partnerID=40&amp;amp;md5=3209980c2ad4abab645518b8af3a7de3&lt;/a&gt;</text>
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              <text>Restricted Access</text>
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              <text>ISSN: 15504646</text>
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              <text>Online</text>
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              <text>Upreti K., CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Kapoor A., Maharaja Surajmal Institute of Technology, Delhi, India; Hundekari S., MIT ADT University, Pune, India; Upreti S., Maharishi Markandeshwar (Deemed to be University), Haryana, Ambala, Mullana, India; Kaul K., Bharati Vidyapeeth College of Engineering, Rohini, India; Kapoor S., Dr. Akhilesh Das Gupta Institute of Technology and Management, New Delhi, India; Tiwari A., CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India</text>
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