Retinal Vessel Detection Using Residual Y-net
2021 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1679903
ABSTRACT
In this Corona Pandemic, Diabetic patients are affected a lot. Unfortunately, due to the consumption of steroids, people are affected by mucormycosis, which is a kind of fungal infection, making this situation worse. Patients get swollen red eyes, and diabetic patients are more vulnerable to it, as they suffer from Diabetic Retinopathy. It has become essential to determine the damage caused to the eye to save patients from vision loss. Only doctors can identify how the condition of the eye by physical examination. But this is a tricky and time-consuming job. With the help of fundus photography and deep learning algorithms, the detection and classification process will speed up. There are many existing image detection algorithms, but they do not have efficient feature retention and lightweight architecture model. This paper proposes Residual Y-net architecture that works excellently on a balanced medium-size. With the help of segmented features, it acquires reliable features which help in classification. It is a very lightweight architecture inspired by U-net, Deep Residual U-net, and Y-net. The addition of residual units in the network has significantly improved the accuracy rate. It is observed that a balanced dataset gives a much accurate performance than an unbalanced dataset. The proposed model's test accuracies on medium-size unbalanced and balanced datasets are 90.39% and 93.60%, respectively. © 2021 IEEE.
CNN-Convolutional Neural Network; DR-Diabetic Retinopathy; Res U-net Residual U-net; Res Y-net-Residual Y-net; Convolutional neural networks; Deep learning; Network architecture; Balanced datasets; Convolutional neural network; Diabetic retinopathy; Diabetics patients; Lightweight architecture; Medium size; Eye protection
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2021 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021
Year:
2021
Document Type:
Article
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