Oxyhaemoglobin saturation NIR-IIb imaging for assessing cancer metabolism and predicting the response to immunotherapy

Nature Nanotechnology, Год журнала: 2023, Номер 19(1), С. 124 - 130

Опубликована: Сен. 11, 2023

Язык: Английский

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Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Biomimetics, Год журнала: 2023, Номер 8(2), С. 235 - 235

Опубликована: Июнь 3, 2023

Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With rise development machine learning deep methods, swarm intelligence algorithms have become research direction, combining image with new effective improvement method. Swarm algorithm refers to an intelligent computing method formed by simulating evolutionary laws, behavior characteristics, thinking patterns insects, birds, natural phenomena, other biological populations. It efficient parallel global optimization capabilities strong performance. In this paper, ant colony algorithm, particle sparrow search bat thimble are deeply studied. The model, features, strategies, application fields processing, such as segmentation, matching, classification, feature extraction, edge detection, comprehensively reviewed. theoretical research, analyzed compared. Combined current literature, methods above comprehensive summarized. representative combined segmentation extracted for list analysis summary. Then, unified framework, common different differences summarized, existing problems raised, finally, future trend is projected.

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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An oppositional-Cauchy based GSK evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for COVID-19 diagnosis

Applied Soft Computing, Год журнала: 2021, Номер 111, С. 107675 - 107675

Опубликована: Июль 10, 2021

Язык: Английский

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A retinal blood vessel segmentation based on improved D-MNet and pulse-coupled neural network

Biomedical Signal Processing and Control, Год журнала: 2021, Номер 73, С. 103467 - 103467

Опубликована: Дек. 27, 2021

Язык: Английский

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Retinal Vessel Segmentation, a Review of Classic and Deep Methods

Annals of Biomedical Engineering, Год журнала: 2022, Номер 50(10), С. 1292 - 1314

Опубликована: Авг. 25, 2022

Язык: Английский

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An Automated Image Segmentation and Useful Feature Extraction Algorithm for Retinal Blood Vessels in Fundus Images

Electronics, Год журнала: 2022, Номер 11(9), С. 1295 - 1295

Опубликована: Апрель 19, 2022

The manual segmentation of the blood vessels in retinal images has numerous limitations. It is very time consuming and prone to human error, particularly with a twisted structure vessel vast number that needs be analysed. Therefore, an automatic algorithm for segmenting extracting useful clinical features from critical help ophthalmologists eye specialists diagnose different diseases assess early treatment. An accurate, rapid, fully measurement fundus proposed improve diagnosis precision decrease workload ophthalmologists. main pipeline composed two essential stages: image extraction stage. Several comprehensive experiments were carried out performance developed automated detecting using extremely challenging datasets, named DRIVE HRF. Initially, accuracy was evaluated terms adequately vessels. In these experiments, five quantitative performances measured calculated validate efficiency algorithm, which consist Acc., Sen., Spe., PPV, NPV measures compared current state-of-the-art approaches on dataset. results obtained showed significantly improvement by achieving 99.55%, 99.93%, 99.09%, 93.45%, 98.89, respectively.

Язык: Английский

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An Efficient Multilevel Thresholding Scheme for Heart Image Segmentation Using a Hybrid Generalized Adversarial Network

Journal of Sensors, Год журнала: 2022, Номер 2022, С. 1 - 11

Опубликована: Ноя. 8, 2022

Most people worldwide, irrespective of their age, are suffering from massive cardiac arrest. To detect heart attacks early, many researchers worked on the clinical datasets collected different open-source like PubMed and UCI repository. However, most these have nearly 13 to 147 raw attributes in textual format implemented traditional data mining approaches. Traditional machine learning approaches just analyze extracted images, but extraction mechanism is inefficient it requires more number resources. The authors this research article proposed a system that aimed at predicting by integrating techniques computer vision deep images labs, which publicly available KAGGLE live scanning through IoT sensors. primary focus enhance quality quantity passing two popular components GAN. GAN introduces noise tries replicate real-time scenarios. Subsequently, newly created segmented applying multilevel threshold operation find region interest. This step helps predict accurate attack rate considering various factors. Earlier obtained sound accuracy generating similar found ROI parts 2D echo images. methodology has achieved an 97.33% 90.97% true-positive rate. reason for selecting computed tomography (CT-SCAN) due gray scale giving reliable information low computational cost.

Язык: Английский

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Human treelike tubular structure segmentation: A comprehensive review and future perspectives

Computers in Biology and Medicine, Год журнала: 2022, Номер 151, С. 106241 - 106241

Опубликована: Окт. 27, 2022

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such are intrathoracic airways, retinal blood vessels, and hepatic vessels. Large collections 2D 3D images have been made available by medical imaging modalities as magnetic resonance (MRI), computed tomography (CT), Optical coherence (OCT) ultrasound the spatial arrangement can be observed. Segmentation these is great importance since analysis structure provides insights into disease diagnosis, treatment planning, prognosis. Manually labelling extensive data radiologists time-consuming error-prone. As result, automated or semi-automated computational models become popular research field past two decades, many developed to date. In this survey, we aim provide comprehensive review currently publicly datasets, segmentation algorithms, evaluation metrics. addition, current challenges future directions discussed.

Язык: Английский

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Automated diagnostic classification of diabetic retinopathy with microvascular structure of fundus images using deep learning method

Biomedical Signal Processing and Control, Год журнала: 2023, Номер 88, С. 105616 - 105616

Опубликована: Окт. 21, 2023

Язык: Английский

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