Cited by RAGE-Net: Enhanced retinal vessel segmentation U-shaped network using Gabor convolution

GCFormer: Multi-scale feature plays a crucial role in medical images segmentation DOI

Yuncong Feng,

Yeming Cong,

Shuaijie Xing

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 300, P. 112170 - 112170

Published: June 27, 2024

Language: Английский

Citations

Enhancing ROP plus form diagnosis: An automatic blood vessel segmentation approach for newborn fundus images DOI

José Guilherme de Almeida, Jan Kubíček, Marek Penhaker

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103054 - 103054

Published: Oct. 8, 2024

Language: Английский

Citations

Deep learning for retinal vessel segmentation: a systematic review of techniques and applications DOI

Zhihui Liu, Mohd Shahrizal Sunar, Tan Tian Swee

et al.

Medical & Biological Engineering & Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 18, 2025

Language: Английский

Citations

EANet: Integrate Edge Features and Attention Mechanisms Multi‐Scale Networks for Vessel Segmentation in Retinal Images DOI

Jiangyi Zhang,

Yuxin Tan,

Duantengchuan Li

et al.

IET Image Processing, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

ABSTRACT Accurately extracting blood vessel structures from retinal fundus images is critical for the early diagnosis and treatment of various ocular systemic diseases. However, segmentation continues to face significant challenges. Firstly, capturing boundary information small vessels particularly difficult. Secondly, uneven thickness irregular distribution further complicate multi‐scale feature modelling. Lastly, low‐contrast lead increased background noise, affecting accuracy. To tackle these challenges, this article presents a network that combines edge features attention mechanisms, referred as EANet. It demonstrates advantages over existing methods. Specifically, EANet consists three key modules: enhancement module, interaction encoding multi‐class mechanism decoding module. Experimental results validate effectiveness method. outperforms advanced methods in precise effectively filtering noise maintain continuity.

Language: Английский

Citations

Punctured window based multiscale line detector for efficient segmentation of retinal blood vessels DOI

Varun Makkar,

Arya Tewary,

Brajesh Kumar

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 191, P. 110155 - 110155

Published: April 16, 2025

Language: Английский

Citations

LMFR-Net: lightweight multi-scale feature refinement network for retinal vessel segmentation DOI

Wenhao Zhang, Shaojun Qu,

YueWen Feng

et al.

Pattern Analysis and Applications, Journal Year: 2025, Volume and Issue: 28(2)

Published: Feb. 13, 2025

Language: Английский

Citations

Skeleton-guided multi-scale dual-coordinate attention aggregation network for retinal blood vessel segmentation DOI

Wei Zhou, Xiaorui Wang, Xuekun Yang

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 181, P. 109027 - 109027

Published: Aug. 23, 2024

Language: Английский

Citations

Advanced Detection of Diabetic Retinopathy: Employing Hybrid Deep Neural Networks and Multi-Scale Image Analysis Techniques DOI

A. M. Mutawa,

G. R. Hemalakshmi,

N. B. Prakash

et al.

Published: Jan. 30, 2024

Diabetic retinopathy (DR), a major complication of prolonged diabetes, poses significant risk vision loss. Early detection is critical for effective treatment, yet traditional diagnostic methods by ophthalmologists are time-consuming, costly, and subject to variability. This study introduces novel approach employing hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier integrated with Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features enhanced DR detection. We implemented advanced image preprocessing techniques, including noise reduction, morphological operations, Otsu’s thresholding, optimize blood vessel segmentation from retinal images. Our method demonstrates exceptional performance in screening DR, achieving an average 96.10% precision, 95.35% sensitivity, 97.06% specificity, accuracy. These results significantly outperform offer promising tool remote efficient DR. Applied publicly available datasets, this research contributes the development accessible, accurate ophthalmology, potentially reducing global burden diabetic

Language: Английский

Citations

Gabor-net with multi-scale hierarchical fusion of features for fundus retinal blood vessel segmentation DOI

Tao Fang, Zhefei Cai,

Yingle Fan

et al.

Journal of Applied Biomedicine, Journal Year: 2024, Volume and Issue: 44(2), P. 402 - 413

Published: April 1, 2024

Language: Английский

Citations

Optimization of intelligent guided vehicle vision navigation based on improved YOLOv2 DOI

Lei Hua, Xing Wu, Jinwang Gu

et al.

Review of Scientific Instruments, Journal Year: 2024, Volume and Issue: 95(6)

Published: June 1, 2024

Addressing the challenge of limited accuracy and real-time performance in intelligent guided vehicle (IGV) image recognition detection, typically reliant on traditional feature extraction approaches. This study delves into a visual navigation detection method using an improved You Only Look Once (YOLO) model–simplified YOLOv2 (SYOLOv2) to satisfy complex operating conditions port limitations IGV hardware computing. The convolutional neural network structure is refined ensure adaptability varying weather single image. Preprocessing images involves Contrast Limited Adaptive Histogram Equalization (CLAHE), while adaptive resolution model, contingent upon speed, proposed enhance performance. comparative experiments conducted datasets reflective actual road demonstrate notable enhancements frames transmitted per second compared conventional methods. These improvements signify efficacy approach meeting stringent requirements for platforms.

Language: Английский

Citations