Diabetic retinopathy detection using image processing and machine learning techniques
— Vision related eye complications are mostly seen in the working-age diabetic population and are termed as Diabetic Retinopathy (DR). Identification of this disease at an early stage helps in preventing from growing further. The study over here describes the application of Convolutional Neural...
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| Main Authors: | , , , , , |
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| Format: | Proceeding Paper |
| Language: | en |
| Published: |
IEEE
2025
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/123202/2/123202_Diabetic%20retinopathy%20detection.pdf http://irep.iium.edu.my/123202/ https://ieeexplore.ieee.org/document/11120145 |
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| Summary: | — Vision related eye complications are mostly seen in
the working-age diabetic population and are termed as
Diabetic Retinopathy (DR). Identification of this disease at an
early stage helps in preventing from growing further. The
study over here describes the application of Convolutional
Neural Networks (CNN) on concealing fundus pictures for
predicting DR. Furthermore, the paper examined multinomial
order diseases and estimated all the mistakes that were
obtained because of misclassification of diseases or due to SVM
and strategic relapse failure to recognize unpretentious
diseases. Moreover, it has been found that pre-treatment
methods in contrast with the adaptable histogram levelling
procedure gave better results and assured dataset constancy by
final check of multinomial order models. Furthermore, the
paper developed the acknowledgement of unremarkable key
points. Moreover, when learning is done on pre-trained
ImageNet models like GoogLeNet, and AlexNet, the
correctness of the characterization models has been seen as
75%, 69%, and 57.2% for 2-ary, 3-ary, and 4-ary models
respectively. The proposed model is an SVM-based model and
it analyses and integrates the various posterior layers of the
retina called fundus images. The results show that this
proposed model can revolve around various disease stages. In
this way, the model makes estimates with 90% correctness. The
proposed neural model accomplished the test with an approval
affectability of 94%. |
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