Diabetic Retinopathy Detection Through Deep Learning Using CNN Wide-Net-X architecture

نویسندگان

چکیده

In recent times, Diabetic Retinopathy (DR) has emerged as a critical complication for patients with diabetes, where the blood vessels in retina are severely damaged, potentially leading to vision loss and, if left untreated, blindness. The World Health Organization projected that by 2040, DR will impact around 224 million people. To address this issue, research paper proposes CNN Wide-Net-X architecture model image classification, which utilizes colour fundus images detect Retinopathy. objective of is enhance accuracy and efficiency diagnostic process. For training testing model, EyePACS dataset consisting 5220 utilized, widely accepted detecting evaluate performance our we use metrics such accuracy, precision, recall, F1-score. proposed significant step towards early detection accurate diagnosis DR. It hoped increased provided can receive timely treatment, thereby reducing risk

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ژورنال

عنوان ژورنال: International journal of engineering technology and management sciences

سال: 2023

ISSN: ['2581-4621']

DOI: https://doi.org/10.46647/ijetms.2023.v07i03.044