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Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review.

Stewart Muchuchuti1, Serestina Viriri1

  • 1School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4001, South Africa.

Journal of Imaging
|April 27, 2023
PubMed
Summary
This summary is machine-generated.

Automated detection of retinal diseases using deep learning shows promise for preventing blindness. Further research into ensemble models and explainability is needed to enhance clinical trust and application.

Keywords:
convolutional neural networksdeep learningdiabetic retinopathyglaucomahypertensive retinopathymacula degenerationretinal disease classification

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Millions suffer from retinal abnormalities, leading to preventable blindness.
  • Manual diagnosis is slow, subjective, and lacks reproducibility.
  • Deep learning models like DCNNs and ViTs offer automated solutions for ocular disease detection.

Purpose of the Study:

  • To review common retinal pathologies and imaging techniques.
  • To critically evaluate deep learning research for diagnosing major retinal diseases.
  • To assess the potential of AI in computer-aided diagnosis (CAD) for ophthalmology.

Main Methods:

  • Literature review of deep learning applications in retinal disease detection.
  • Analysis of current research on DCNNs and ViTs for glaucoma, diabetic retinopathy, and AMD.
  • Evaluation of imaging modalities used in conjunction with AI models.

Main Results:

  • Deep learning models demonstrate significant potential in automated retinal disease detection and grading.
  • Challenges remain due to the complexity of retinal lesions.
  • Computer-Aided Diagnosis (CAD) is emerging as a crucial assistive technology.

Conclusions:

  • Deep learning-based CAD is vital for supporting clinicians in diagnosing retinal conditions.
  • Future work should focus on ensemble CNN architectures for complex, multi-class tasks.
  • Improving model explainability is essential for clinical adoption and trust.