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

AUTHOR(S)

T.Kavitha, Basude Rohith, Akul Lakha, Akshay Chelikani

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

ABSTRACT
In recent times, Diabetic Retinopathy (DR) has emerged as a critical complication for patients with diabetes, where the blood vessels in the retina are severely damaged, potentially leading to vision loss and, if left untreated, blindness. The World Health Organization has projected that by 2040, DR will impact around 224 million people. To address this issue, this research paper proposes CNN Wide-Net-X architecture model for image classification, which utilizes colour fundus images to detect Diabetic Retinopathy. The objective of this model is to enhance the accuracy and efficiency of the diagnostic process. For training and testing the model, the EyePACS dataset consisting of 5220 images is utilized, which is a widely accepted dataset for detecting Diabetic Retinopathy. To evaluate the performance of our model, we use metrics such as accuracy, precision, recall, and F1-score. The proposed CNN model is a significant step towards early detection and accurate diagnosis of DR. It is hoped that with the increased accuracy and efficiency provided by this model, patients with DR can receive timely treatment, thereby reducing the risk of vision loss and blindness.
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How to Cite This Article:
T.Kavitha, Basude Rohith, Akul Lakha, Akshay Chelikani .Diabetic Retinopathy Detection Through Deep Learning Using CNN Wide-Net-X architecture . ijetms;7(3):333-339. DOI: 10.46647/ijetms.2023.v07i03.044