A systematic review of retinal fundus image segmentation and classification methods using convolutional neural networks

Healthcare Analytics, Journal Year: 2023, Volume and Issue: 4, P. 100261 - 100261

Published: Sept. 22, 2023

Retinal fundus images play a crucial role in the early detection of eye problems, aiding timely diagnosis and treatment to prevent vision loss or blindness. With advancements technology, Convolutional Neural Network (CNN) algorithms have emerged as effective tools for recognition, delineation, classification tasks. This study proposes comprehensive review CNN used retinal image segmentation classification. Our follows systematic approach, exploring diverse repositories identify studies employing segment classify images. Utilizing CNNs can enhance precision outcomes alleviate sole dependence on human experts. approach enables more accurate results, reducing burden A total sixty-two are included our review, analyzing aspects such database usage advantages disadvantages methods employed. The provides valuable insights, limitations, observations, future directions field. Despite certain findings indicate that consistently achieve high accuracies. examination sheds light potential analysis.

Language: Английский

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HRD-Net: High resolution segmentation network with adaptive learning ability of retinal vessel features

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 173, P. 108295 - 108295

Published: March 19, 2024

Language: Английский

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Grading diabetic retinopathy using multiresolution based CNN

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 86, P. 105210 - 105210

Published: July 3, 2023

Language: Английский

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Retinal image blood vessel classification using hybrid deep learning in cataract diseased fundus images

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 84, P. 104776 - 104776

Published: March 10, 2023

Language: Английский

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Retinal vessel segmentation to diagnose diabetic retinopathy using fundus images: A survey

International Journal of Imaging Systems and Technology, Journal Year: 2023, Volume and Issue: 34(1)

Published: Aug. 7, 2023

Abstract Diabetes can cause damage to the retina's blood vessels in eye leading diabetic retinopathy (DR). The images captured using a fundus camera are used segment and study vessel damage. Once retina is damaged, it cannot be repaired. Therefore, early detection of only way control progression disease. It allows physicians provide timely appropriate treatment patients. ophthalmologist manually recognize mark these based on some clinical geometrical features; however, time‐consuming. Extraction segmentation play significant role showing difference between healthy newly developed abnormal vessels. Due increased diabetes population, automated systems have been designed detect retinal assist ophthalmologists. Identifying, extracting, examining intricate processes, specifically identifying new retina. In medical image analysis, artificial intelligence deep learning techniques become widely practices for automatic segmentation. We reviewed articles from 1989 2023, including handcrafted recent deep‐learning with available public datasets. concluded this article an overview observed parameters, calculations, future directions analysis images. believe review will help researchers identify research gaps field DR.

Language: Английский

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A comprehensive survey on segmentation techniques for retinal vessel segmentation

Neurocomputing, Journal Year: 2023, Volume and Issue: 556, P. 126626 - 126626

Published: Aug. 4, 2023

Language: Английский

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VisionDeep-AI: Deep learning-based retinal blood vessels segmentation and multi-class classification framework for eye diagnosis

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 94, P. 106273 - 106273

Published: March 28, 2024

Language: Английский

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A unified 2D medical image segmentation network (SegmentNet) through distance-awareness and local feature extraction

Journal of Applied Biomedicine, Journal Year: 2024, Volume and Issue: 44(3), P. 431 - 449

Published: June 13, 2024

Language: Английский

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Automatic vessel crossing and bifurcation detection based on multi-attention network vessel segmentation and directed graph search

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 155, P. 106647 - 106647

Published: Feb. 15, 2023

Analysis of the vascular tree is basic premise to automatically diagnose retinal biomarkers associated with ophthalmic and systemic diseases, among which accurate identification intersection bifurcation points quite challenging but important for disentangling complex network tracking vessel morphology. In this paper, we present a novel directed graph search-based multi-attentive neural approach segment separate intersections bifurcations from color fundus images. Our uses multi-dimensional attention adaptively integrate local features their global dependencies while learning focus on target structures at different scales generate binary maps. A graphical representation constructed represent topology spatial connectivity structures. Using geometric information including difference, diameter, angle, decomposed into multiple sub-trees finally classify label feature points. The proposed method has been tested DRIVE dataset IOSTAR containing 40 images 30 images, respectively, 0.863 0.764 F1-score detection average accuracy 0.914 0.854 classification These results demonstrate superiority our outperforming state-of-the-art methods in point classification.

Language: Английский

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A multitask learning network with interactive fusion for surgical instrument segmentation

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113370 - 113370

Published: March 1, 2025

Language: Английский

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