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: Dec 6, 2025

In Vivo Multimodal Imaging and Analysis of Mouse Laser-Induced Choroidal Neovascularization Model
09:56

In Vivo Multimodal Imaging and Analysis of Mouse Laser-Induced Choroidal Neovascularization Model

Published on: January 21, 2018

9.7K

Diabetic Retinopathy (DR) Severity Level Classification Using Multimodel Convolutional Neural Networks.

Ali J Abidalkareem, Moaed A Abd, Ali K Ibrahim

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary
    This summary is machine-generated.

    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

    Contrastive learning for passive acoustic monitoring: A framework for sound source discovery and cross-site comparison in marine soundscapes.

    PLoS computational biology·2026
    Same author

    Semi-supervised synthesis of 7T MRI from 3T using 3D FR-U-Net with anatomical segmentation consistency assessment.

    PloS one·2025
    Same author

    Hydrophone placement yields high variability in detection of Epinephelus striatus calls at a spawning site.

    Ecological applications : a publication of the Ecological Society of America·2025
    Same author

    A Multimodal Multi-Stage Deep Learning Model for the Diagnosis of Alzheimer's Disease Using EEG Measurements.

    Neurology international·2025
    Same author

    Identification of Gene Expression in Different Stages of Breast Cancer with Machine Learning.

    Cancers·2024
    Same author

    Biohybrid Robotic Hand to Investigate Tactile Encoding and Sensorimotor Integration.

    Biomimetics (Basel, Switzerland)·2024

    This study introduces a new AI tool for detecting diabetic retinopathy (DR) from eye images. The proposed method achieves 93.2% accuracy, significantly outperforming existing models for early DR diagnosis.

    Area of Science:

    • Ophthalmology
    • Artificial Intelligence
    • Medical Imaging

    Background:

    • Diabetic retinopathy (DR) is a leading cause of blindness in working-age adults.
    • Early detection and diagnosis of DR are crucial for preventing irreversible vision loss.
    • Current diagnostic methods require specialized personnel and facilities.

    Purpose of the Study:

    • To develop an automated diagnostic tool for detecting diabetic retinopathy from fundus images.
    • To evaluate the performance of a proposed ensemble of multi-inception Convolutional Neural Networks (CNNs).

    Main Methods:

    • Utilized an ensemble of multi-inception CNN networks for DR detection.
    • The inception block incorporated 3x3, 5x5, and 1x1 convolutional layers.
    • Compared the proposed method against pre-trained VGG16 and GoogleNet models.

    More Related Videos

    Using Retinal Imaging to Study Dementia
    09:17

    Using Retinal Imaging to Study Dementia

    Published on: November 6, 2017

    22.1K
    Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature
    05:54

    Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature

    Published on: January 24, 2025

    553

    Related Experiment Videos

    Last Updated: Dec 6, 2025

    In Vivo Multimodal Imaging and Analysis of Mouse Laser-Induced Choroidal Neovascularization Model
    09:56

    In Vivo Multimodal Imaging and Analysis of Mouse Laser-Induced Choroidal Neovascularization Model

    Published on: January 21, 2018

    9.7K
    Using Retinal Imaging to Study Dementia
    09:17

    Using Retinal Imaging to Study Dementia

    Published on: November 6, 2017

    22.1K
    Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature
    05:54

    Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature

    Published on: January 24, 2025

    553

    Main Results:

    • The proposed ensemble method achieved an accuracy of 93.2% in detecting diabetic retinopathy.
    • This performance significantly surpassed the 81% accuracy obtained using transfer learning with VGG19.
    • Demonstrated the effectiveness of the multi-inception CNN architecture.

    Conclusions:

    • The developed AI tool shows high accuracy and potential for early diabetic retinopathy detection.
    • This automated approach could improve accessibility and efficiency in DR diagnosis.
    • Further research can refine the model for clinical application.