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

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 94, С. 106273 - 106273

Опубликована: Март 28, 2024

Язык: Английский

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Dual-channel asymmetric convolutional neural network for an efficient retinal blood vessel segmentation in eye fundus images

Journal of Applied Biomedicine, Год журнала: 2022, Номер 42(2), С. 695 - 706

Опубликована: Апрель 1, 2022

Язык: Английский

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DilUnet: A U-net based architecture for blood vessels segmentation

Computer Methods and Programs in Biomedicine, Год журнала: 2022, Номер 218, С. 106732 - 106732

Опубликована: Март 5, 2022

Язык: Английский

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Deep matched filtering for retinal vessel segmentation

Knowledge-Based Systems, Год журнала: 2023, Номер 283, С. 111185 - 111185

Опубликована: Ноя. 10, 2023

Язык: Английский

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Optimizing Road Traffic Surveillance: A Robust Hyper-Heuristic Approach for Vehicle Segmentation

IEEE Access, Год журнала: 2024, Номер 12, С. 29503 - 29524

Опубликована: Янв. 1, 2024

Язык: Английский

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Nature and Biologically Inspired Image Segmentation Techniques

Archives of Computational Methods in Engineering, Год журнала: 2021, Номер 29(3), С. 1415 - 1442

Опубликована: Июнь 26, 2021

Язык: Английский

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Automatic melanoma detection using discrete cosine transform features and metadata on dermoscopic images

Journal of King Saud University - Computer and Information Sciences, Год журнала: 2024, Номер 36(2), С. 101944 - 101944

Опубликована: Фев. 1, 2024

Nowadays, raw dermoscopic images in melanoma detection do not have acceptable performance. Machine learning helps detect accurately. There are extensive studies classic and deep learning-based approaches for the literature. Still, they accurate or require high data. This paper proposes a hybrid mechanism automated on based Discrete Cosine Transform features metadata. It is composed of three steps. First, extra information/artifacts deleted; remaining pixels standardized processing. Second, reliability improved by Radon transform, data removed using Top-hat filter, rate increased Wavelet Transform. Then, number reduced Locality Sensitive Discriminant Analysis. The third step divides into test ones to create image-based models Finally, best model selected metadata-based model. Simulation results show that decision tree provides most practical improving accuracy sensitivity. Besides, comparison demonstrate our improves F-Value superior other approaches.

Язык: Английский

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Multi-Scale Three-Path Network (MSTP-Net): A new architecture for retinal vessel segmentation

Measurement, Год журнала: 2025, Номер unknown, С. 117100 - 117100

Опубликована: Март 1, 2025

Язык: Английский

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Automatic Segmentation of Retinal Blood Vessel Using the Homomorphic Filter and Multilevel Algorithm

Cognitive science and technology, Год журнала: 2025, Номер unknown, С. 415 - 424

Опубликована: Янв. 1, 2025

Язык: Английский

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WS-SAM: self-prompting SAM with wavelet and spatial domain for OCTA retinal vessel segmentation

The Journal of Supercomputing, Год журнала: 2025, Номер 81(5)

Опубликована: Март 28, 2025

Язык: Английский

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