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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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Doppler Optical Coherence Tomography of Retinal Circulation
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Published on: September 18, 2012

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Computer-aided diagnosis system for retinal disorder classification using optical coherence tomography images.

Neven Saleh1, Manal Abdel Wahed2, Ahmed M Salaheldin1

  • 1Systems and Biomedical Engineering Department, Higher Institute of Engineering in El-Shorouk city, Shorouk Academy, Cairo, Egypt.

Biomedizinische Technik. Biomedical Engineering
|May 19, 2022
PubMed
Summary

This study developed a computer-aided diagnosis system using Optical Coherence Tomography (OCT) images to detect and classify retinal disorders like diabetic macular edema, achieving high accuracy and outperforming previous research.

Keywords:
SqueezeNetcomputer-aided diagnosisoptical coherence tomographyretinal disease

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Vision impairment incidence is rising, posing challenges for accurate diagnosis and classification of retinal abnormalities.
  • Optical Coherence Tomography (OCT) is a key imaging modality in ophthalmology, but automated analysis remains a challenge.

Purpose of the Study:

  • To develop a computer-aided diagnosis (CAD) system for detecting and classifying retinal disorders using OCT images.
  • To investigate the efficacy of combining deep learning and machine learning algorithms for retinal disease classification.

Main Methods:

  • A modified SqueezeNet neural network was employed for feature extraction from OCT images.
  • Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Decision Tree (DT), and Ensemble Model (EM) were utilized for classification.
  • Bayesian optimization was used for hyperparameter tuning of the classification models.

Main Results:

  • The developed system achieved high classification accuracies: SVM (97.39%), K-NN (97.47%), DT (96.98%), and EM (95.25%).
  • Performance was evaluated using nine criteria across 12,000 OCT images.
  • The system demonstrated superior performance compared to relevant studies in terms of accuracy and sample size.

Conclusions:

  • A novel CAD system for retinal disease detection and classification was successfully developed.
  • The system has the potential to reduce diagnostic errors and save time in clinical ophthalmology.
  • This approach highlights the effectiveness of hybrid deep learning and machine learning models in analyzing OCT data.