Image Fundus Classification System for Diabetic Retinopathy Stage Detection Using Hybrid CNN-DELM

Diabetic retinopathy is the leading cause of blindness suffered by working-age adults. The increase in the population diagnosed with DR can be prevented by screening and early treatment of eye damage. This screening process can be conducted by utilizing deep learning techniques. In this study, the d...

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Bibliographic Details
Published in:Big data and cognitive computing 2022-12, Vol.6 (4), p.146
Main Authors: Novitasari, Dian Candra Rini, Fatmawati, Fatmawati, Hendradi, Rimuljo, Rohayani, Hetty, Nariswari, Rinda, Arnita, Arnita, Hadi, Moch Irfan, Saputra, Rizal Amegia, Primadewi, Ardhin
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Architecture
Blindness
Blood vessels
Classification
CNN architecture
Comparative analysis
Datasets
Deep learning
DELM classification
Diabetes
Diabetic retinopathy
Feature extraction
feature learning
Health aspects
Image classification
Kernel functions
Machine learning
Medical imaging equipment
Methods
Neural networks
Retina
Screening
Support vector machines
Training
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Summary:Diabetic retinopathy is the leading cause of blindness suffered by working-age adults. The increase in the population diagnosed with DR can be prevented by screening and early treatment of eye damage. This screening process can be conducted by utilizing deep learning techniques. In this study, the detection of DR severity was carried out using the hybrid CNN-DELM method (CDELM). The CNN architectures used were ResNet-18, ResNet-50, ResNet-101, GoogleNet, and DenseNet. The learning outcome features were further classified using the DELM algorithm. The comparison of CNN architecture aimed to find the best CNN architecture for fundus image features extraction. This research also compared the effect of using the kernel function on the performance of DELM in fundus image classification. All experiments using CDELM showed maximum results, with an accuracy of 100% in the DRIVE data and the two-class MESSIDOR data. Meanwhile, the best results obtained in the MESSIDOR 4 class data reached 98.20%. The advantage of the DELM method compared to the conventional CNN method is that the training time duration is much shorter. CNN takes an average of 30 min for training, while the CDELM method takes only an average of 2.5 min. Based on the value of accuracy and duration of training time, the CDELM method had better performance than the conventional CNN method.
ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc6040146