ArticleComputers in biology and medicine2022
Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization.
Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed, 157 citations in OpenAlex.
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- Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding.Scientific reports · 2025Article
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- Modeling COVID-19 data with a novel neutrosophic Burr-III distribution.Scientific reports · 2024Article
- Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears.Scientific reports · 2024Article
- Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques.PloS one · 2024Article
- Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images.Computers in biology and medicine · 2023Article
- Hierarchical Harris hawks optimizer for feature selection.Journal of advanced research · 2023Article
- Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter.Journal of cancer research and clinical oncology · 2023Article
- Otsu Multi-Threshold Image Segmentation Based on Adaptive Double-Mutation Differential Evolution.Biomimetics (Basel, Switzerland) · 2023Article
- Metaheuristic Framework for Material Screening and Operating Optimization of Adsorption-Based Heat Pumps.ACS omega · 2023Article
- A novel scale based on biomarkers associated with COVID-19 severity can predict the need for hospitalization and intensive care, as well as enhanced probabilities for mortality.Scientific reports · 2023Article
- A Two-Stage Automatic Color Thresholding Technique.Sensors (Basel, Switzerland) · 2023Article
- Preparing pathological data to develop an artificial intelligence model in the nonclinical study.Scientific reports · 2023Article
- A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images.Computers in biology and medicine · 2023Article
- Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images.Computers in biology and medicine · 2023Article
- Impact of quarantine on fractional order dynamical model of Covid-19.Computers in biology and medicine · 2022Article
- Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost.Computers in biology and medicine · 2022Article
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Authors and funding
9 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is currently raging worldwide, with more patients being diagnosed every day. It usually is diagnosed by examining pathological photographs of the patient's lungs. There is a lot of detailed and essential information on chest radiographs, but manual processing is not as efficient or accurate. As a result, how efficiently analyzing and processing chest radiography of COVID-19 patients is an important research direction to promote COVID-19 diagnosis. To improve the processing efficiency of COVID-19 chest films, a multilevel thresholding image segmentation (MTIS) method based on an enhanced multiverse optimizer (CCMVO) is proposed. CCMVO is improved from the original Multi-Verse Optimizer by introducing horizontal and vertical search mechanisms. It has a more assertive global search ability and can jump out of the local optimum in optimization. The CCMVO-based MTIS method can obtain higher quality segmentation results than HHO, SCA, and other forms and is less prone to stagnation during the segmentation process. To verify the performance of the proposed CCMVO algorithm, CCMVO is first compared with DE, MVO, and other algorithms by 30 benchmark functions; then, the proposed CCMVO is applied to image segmentation of COVID-19 chest radiography; finally, this paper verifies that the combination of MTIS and CCMVO is very successful with good segmentation results by using the Feature Similarity Index (FSIM), the Peak Signal to Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). Therefore, this research can provide an effective segmentation method for a medical organization to process COVID-19 chest radiography and then help doctors diagnose coronavirus pneumonia (COVID-19).
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