Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

SHARP: a hybrid metaheuristic approach for intelligent robotic path planning.

Scientific reports·2026

Same author

Early prediction of diabetic retinopathy using a multimodal deep learning framework integrating fundus and OCT imaging.

Frontiers in medicine·2026

Same author

Corrigendum to "The Use of Facial Expressions in Measuring Students' Interaction with Distance Learning Environments During the COVID-19 Crisis" Visual Informatics, Volume 7, Issue 1, March 2023, Pages 1-17.

Visual informatics·2022

Same author

The use of facial expressions in measuring students' interaction with distance learning environments during the COVID-19 crisis.

Visual informatics·2022

Same author

ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems.

Sensors (Basel, Switzerland)·2022

Same author

A Blockchain-Based Multi-Factor Authentication Model for a Cloud-Enabled Internet of Vehicles.

Sensors (Basel, Switzerland)·2021

Same journal

Integrated multi-assessment and structural performance index framework for stacking-sequence optimisation of natural fibre reinforced laminates.

Scientific reports·2026

Same journal

SuperiorGAT: graph attention networks for sparse LiDAR point cloud reconstruction in autonomous systems.

Scientific reports·2026

Same journal

The effect of stretching the pectoralis major, sternocleidomastoid, and iliopsoas muscles on 800 m swimming performance in master swimmers.

Scientific reports·2026

Same journal

ISNR-PQC: isometry noise resilience post quantum cryptography primitive.

Scientific reports·2026

Same journal

Identification of high-yielding and stable genotypes of barley in the cold climate of Iran using AMMI and GGE biplot models.

Scientific reports·2026

Same journal

Bayesian negative binomial modelling of spatial and temporal patterns of road traffic deaths in Ghana.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 2, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A cognitive deep learning approach for medical image processing.

Hussam N Fakhouri¹, Sadi Alawadi^2,3, Feras M Awaysheh⁴

¹Department of Data Science and Artificial Intelligence, The University of Petra, Amman, Jordan.

Scientific Reports

|February 24, 2024

Summary

This summary is machine-generated.

A new hybrid model, cognitive Deep Learning Retinal Blood Vessel Segmentation (CoDLRBVS), enhances ophthalmic diagnostics by accurately segmenting retinal blood vessels using U-Net and image processing. It sets a new benchmark for retinal vessel segmentation accuracy.

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Jul 2, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Ophthalmic diagnostics
Medical image processing
Computer vision

Background:

Accurate retinal blood vessel segmentation is crucial for diagnosing eye conditions.
Complex retinal image features present significant challenges to existing segmentation methods.
Existing techniques often lack efficiency and precision in complex scenarios.

Purpose of the Study:

To introduce a novel hybrid model, cognitive Deep Learning Retinal Blood Vessel Segmentation (CoDLRBVS), for precise retinal blood vessel segmentation.
To enhance segmentation accuracy and efficiency by integrating deep learning with advanced image processing.
To establish a new benchmark in retinal vessel segmentation performance.

Main Methods:

Developed CoDLRBVS, a hybrid model combining U-Net architecture with image processing techniques.
Integrated a matched filter (MF) for preprocessing and morphological techniques (MT) for post-processing.
Incorporated multi-scale line detection and scale space methods within a cognitive computing framework.

Main Results:

Achieved a mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4%.
Demonstrated superior performance across multiple datasets (DRIVE, STARE, HRF, retinal blood vessel, Chase-DB1).
Established a new benchmark, surpassing existing models in retinal vessel segmentation.

Conclusions:

CoDLRBVS effectively overcomes challenges in retinal blood vessel segmentation.
The hybrid approach offers human-like adaptability and reasoning for improved medical image analysis.
CoDLRBVS shows significant potential for advancing ophthalmic diagnostics and medical image processing.