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Related Concept Videos

Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Retinal fundus imaging-based diabetic retinopathy classification using transfer learning and fennec fox optimization.

Indresh Kumar Gupta1,2, Shruti Patil3,4, Supriya Mahadevkar3

  • 1Pranveer Singh Institute of Technology, Kanpur, U.P, India.

Methodsx
|April 16, 2025
PubMed
Summary
This summary is machine-generated.

Diabetic retinopathy (DR) detection is automated using a novel deep learning model. This advanced system enhances early diagnosis and classification of DR, crucial for preventing vision loss in diabetes patients.

Keywords:
Deep learningDiabetic retinopathyFennec fox optimizationFundus Imaging Diabetic Retinopathy Classification using Deep Learning and Fennec Fox Optimization (FIDRC-DLFFO) modelInception-ResNet-v2Median filter

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Diabetic retinopathy (DR) is a leading cause of vision loss, silently progressing without early symptoms.
  • Early detection via retinal fundus imaging is vital for effective DR management.
  • Manual image analysis is time-consuming, necessitating automated computer-aided diagnosis (CAD) systems.

Purpose of the Study:

  • To introduce the Fundus Imaging Diabetic Retinopathy Classification using Deep Learning and Fennec Fox Optimization (FIDRC-DLFFO) model.
  • To automate the identification and classification of diabetic retinopathy.
  • To enhance the accuracy and efficiency of DR diagnosis.

Main Methods:

  • The FIDRC-DLFFO model employs median filtering for noise reduction and Inception-ResNet-v2 for feature extraction.
  • A gated recurrent unit (GRU) is utilized for classification, with hyperparameters optimized by Fennec Fox Optimization (FFO).
  • The model's performance was evaluated on benchmark datasets.

Main Results:

  • The FIDRC-DLFFO model demonstrated automated DR detection and classification capabilities.
  • Hyperparameter tuning using FFO significantly improved GRU classification accuracy.
  • The study provides evidence for the model's effectiveness in identifying diabetic retinopathy.

Conclusions:

  • The proposed FIDRC-DLFFO model offers an effective automated solution for diabetic retinopathy classification.
  • Optimized deep learning approaches can significantly improve diagnostic accuracy in medical imaging.
  • This model shows potential for real-world clinical application in supporting ophthalmologists and improving patient outcomes.