Parrot optimizer: Algorithm and applications to medical problems

Computers in Biology and Medicine, Год журнала: 2024, Номер 172, С. 108064 - 108064

Опубликована: Фев. 24, 2024

Язык: Английский

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Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools

Computers in Biology and Medicine, Год журнала: 2022, Номер 152, С. 106405 - 106405

Опубликована: Дек. 7, 2022

Язык: Английский

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An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm

Computers in Biology and Medicine, Год журнала: 2022, Номер 149, С. 106075 - 106075

Опубликована: Сен. 6, 2022

Язык: Английский

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A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images

Computers in Biology and Medicine, Год журнала: 2022, Номер 152, С. 106404 - 106404

Опубликована: Дек. 6, 2022

Язык: Английский

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Recent progress in transformer-based medical image analysis

Computers in Biology and Medicine, Год журнала: 2023, Номер 164, С. 107268 - 107268

Опубликована: Июль 20, 2023

Язык: Английский

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Hierarchical Harris hawks optimizer for feature selection

Journal of Advanced Research, Год журнала: 2023, Номер 53, С. 261 - 278

Опубликована: Янв. 20, 2023

Feature selection is a typical NP-hard problem. The main methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must swarm intelligence algorithm, performance in feature closely related to algorithm's quality. Therefore, it essential choose design suitable algorithm improve based on wrapper. Harris hawks optimization (HHO) superb approach that has just been introduced. It high convergence rate powerful global search capability but an unsatisfactory effect dimensional problems or complex problems. we introduced hierarchy HHO's ability deal with selection. To make obtain good accuracy fewer features run faster selection, improved HHO named EHHO. On 30 UCI datasets, (EHHO) can achieve very classification less running time features. We first conducted extensive experiments 23 classical benchmark functions compared EHHO many state-of-the-art metaheuristic algorithms. Then transform into binary (bEHHO) through conversion function verify extraction data sets. Experiments show better speed minimum than other peers. At same time, HHO, significantly weakness dealing functions. Moreover, datasets repository, bEHHO comparative Compared original bHHO, excellent also bHHO time.

Язык: Английский

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LDANet: Automatic lung parenchyma segmentation from CT images

Computers in Biology and Medicine, Год журнала: 2023, Номер 155, С. 106659 - 106659

Опубликована: Фев. 10, 2023

Язык: Английский

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Autism Spectrum Disorder detection framework for children based on federated learning integrated CNN-LSTM

Computers in Biology and Medicine, Год журнала: 2023, Номер 166, С. 107539 - 107539

Опубликована: Окт. 4, 2023

Язык: Английский

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BCU-Net: Bridging ConvNeXt and U-Net for medical image segmentation

Computers in Biology and Medicine, Год журнала: 2023, Номер 159, С. 106960 - 106960

Опубликована: Апрель 20, 2023

Язык: Английский

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FATA: An efficient optimization method based on geophysics

Neurocomputing, Год журнала: 2024, Номер 607, С. 128289 - 128289

Опубликована: Авг. 3, 2024

Язык: Английский

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