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An Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost.

Yinan Zhang1, Mingqiang An2

  • 1School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China; College of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, China.

Computational and Mathematical Methods in Medicine
|September 24, 2016
PubMed
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This study introduces an active learning classifier to reduce diabetic retinopathy (DR) screening costs. The method uses fewer labeled images, achieving high accuracy and saving expenses.

Area of Science:

  • Ophthalmology
  • Computer Science
  • Machine Learning

Background:

  • Diabetic retinopathy (DR) screening is crucial but costly.
  • Reducing DR screening expenses is a significant challenge in healthcare.

Purpose of the Study:

  • To propose an active learning classifier to decrease the financial burden of DR screening.
  • To enhance the efficiency of DR screening using machine learning techniques.

Main Methods:

  • Feature extraction using anatomical part recognition and lesion detection algorithms.
  • Implementation of a Kernel Extreme Learning Machine (KELM) classifier.
  • Integration of active learning and ensemble techniques to improve KELM performance with limited data.

Main Results:

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  • The proposed classifier achieved superior accuracy compared to traditional methods (CART, SVM variants, KNN) when trained on only 20%-35% of labeled retinal images.
  • Comparative classifiers required 80% of labeled data to achieve lower accuracy.
  • The active learning approach minimized the need for manual annotation by medical professionals.

Conclusions:

  • The active learning classifier is an efficient and cost-effective solution for DR screening.
  • This approach significantly reduces the financial requirements for DR screening systems.
  • The method demonstrates the potential to improve accessibility to DR screening through reduced costs.