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Related Experiment Video

Updated: May 5, 2026

Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression
04:36

Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression

Published on: January 12, 2024

1.9K

A Novel Convolutional Neural Network for Explainable Diabetic Retinopathy Detection and Grade Identification.

Simona Correra1, Valeria Sorgente1, Mario Cesarelli2

  • 1Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, 86100 Campobasso, Italy.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

Related Concept Videos

Diabetic Retinopathy01:27

Diabetic Retinopathy

55
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...
55

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This summary is machine-generated.

This study introduces an explainable deep learning method for detecting diabetic retinopathy severity in retinal images. The novel FGNet architecture shows promise for early clinical assessment and preventing blindness.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Diabetic retinopathy is a leading cause of global blindness.
  • Early diagnosis is crucial for effective intervention.
  • Automated detection methods can aid clinical assessment.

Purpose of the Study:

  • To develop an explainable deep learning method for automatic diabetic retinopathy severity identification.
  • To evaluate the performance of various convolutional neural network architectures, including a novel FGNet.
  • To provide visual insights into model predictions for clinical support.

Main Methods:

  • Utilized deep learning with multiple CNN architectures (VGG16, StandardCNN, ResNet, CustomCNN, EfficientNet, MobileNet, FGNet).
  • Developed and trained a novel architecture, FGNet, specifically for diabetic retinopathy detection.
Keywords:
artificial intelligenceclassificationconvolutional neural networkdeep learningdiabetic retinopathy

Related Experiment Videos

Last Updated: May 5, 2026

Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression
04:36

Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression

Published on: January 12, 2024

1.9K
  • Employed Gradient-weighted Class Activation Mapping (Grad-CAM) for model explainability.

    • Main Results:

    • The proposed FGNet achieved an accuracy of 0.75 after 10 epochs and 0.71 after 20 epochs.
    • Explainability techniques provided visual insights into the model's decision-making process.
    • The method demonstrated potential for supporting early clinical assessment.

    Conclusions:

    • The developed explainable AI method shows potential for accurate diabetic retinopathy severity detection.
    • FGNet offers a promising approach for automated analysis of retinal images.
    • Explainable AI in this context can enhance clinical decision-making and patient outcomes.