Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Feb 18, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.9K

A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning

Umit Budak1, Abdulkadir Şengür2, Yanhui Guo3

  • 1Electrical-Electronics Engineering Department, Engineering Faculty, Bitlis Eren University, Bitlis, Turkey.

Health Information Science and Systems
|November 18, 2017
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

DenseViT-OCT: A Hybrid CNN-Transformer Architecture with Multi-Scale Dense Feature Aggregation for Automated Epiretinal Membrane Severity Classification.

Tomography (Ann Arbor, Mich.)·2026
Same author

An F1-score-weighted ensemble of deep learning models for enhanced cloud detection in remote sensing imagery.

Scientific reports·2026
Same author

Query-Driven Retinal Layer Segmentation in OCT Using Cross-Attentive Feature Learning.

Diagnostics (Basel, Switzerland)·2026
Same author

An Iron-Based MRI Probe with Tunable Spin State for Bioorthogonally Catalyzed Activation Imaging.

Journal of the American Chemical Society·2026
Same author

Activating lattice oxygen in metal oxyhydroxides as durable electrodes for industrial-scale seawater splitting.

Nature communications·2026
Same author

Automatic Infant Movement Assessment Using Pose-LBP Features and a Cost-Sensitive Subspace kNN Ensemble.

Bioengineering (Basel, Switzerland)·2026
Same journal

AutoBiGluNet: transformer-based time series modeling for blood glucose prediction in Type 1 diabetes patients.

Health information science and systems·2026
Same journal

Multi-dimensional alignment framework with geometric intraoral constraints for precise occlusal registration.

Health information science and systems·2026
Same journal

SPSGL: uncovering psychiatric network mechanisms via structural-prior guided synaptic graph learning.

Health information science and systems·2026
Same journal

A noval 4D graph temporal brain network model for EEG-based depression detection.

Health information science and systems·2026
Same journal

PLETHSOMNet: automated identification of insomnia using deep neural network technique with photoplethysmography (PPG) signals.

Health information science and systems·2026
Same journal

Self-supervised fusion of clinical expertise and interpersonal skills for enhanced physician recommendation.

Health information science and systems·2026
See all related articles
This summary is machine-generated.

This study introduces an automated system for detecting microaneurysms (MAs), early signs of diabetic retinopathy, in fundus images. The method utilizes deep convolutional neural networks for accurate identification, reducing reliance on manual analysis.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Diabetic retinopathy (DR) is a leading cause of vision loss.
  • Microaneurysms (MAs) are early indicators of DR, appearing as red lesions in fundus images.
  • Current MA detection methods require expert physicians or invasive, costly eye angiography.

Purpose of the Study:

  • To develop an automated system for detecting microaneurysms (MAs) in color fundus images.
  • To provide an accessible and efficient alternative to manual MA detection.
  • To improve early diagnosis of diabetic retinopathy.

Main Methods:

  • A three-stage approach involving image pre-processing, candidate MA extraction, and classification.
  • Pre-processing includes green channel decomposition, Gaussian and median filtering, and background subtraction.
Keywords:
Color fundus imagesDeep convolutional neural networkDiabetic retinopathyMicroaneurysms detectionReinforcement sample learning strategy

Related Experiment Videos

Last Updated: Feb 18, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.9K
  • A deep convolutional neural network (DCNN) with reinforcement learning is employed for final MA detection, trained on labeled image patches.

    • Main Results:

    • The proposed system successfully detected microaneurysms in color fundus images.
    • Experimental results on the ROC dataset demonstrated the system's effectiveness.
    • The automated approach shows promise in identifying early signs of diabetic retinopathy.

    Conclusions:

    • The developed automated system offers a viable solution for microaneurysm detection.
    • This technology can aid in the early diagnosis and management of diabetic retinopathy.
    • Further validation and integration into clinical workflows are warranted.