ArticleComputers in biology and medicine2022
Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation.
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 31 papers.
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31 citing papers in PubMed.
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- A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma.Scientific reports · 2024Article
- Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears.Scientific reports · 2024Article
- Design and optimization of haze prediction model based on particle swarm optimization algorithm and graphics processor.Scientific reports · 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
- Effective Invasiveness Recognition of Imbalanced Data by Semi-Automated Segmentations of Lung Nodules.Biomedicines · 2023Article
- An Enhanced RIME Optimizer with Horizontal and Vertical Crossover for Discriminating Microseismic and Blasting Signals in Deep Mines.Sensors (Basel, Switzerland) · 2023Article
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- 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
- Multilevel Threshold Segmentation of Skin Lesions in Color Images Using Coronavirus Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images.Biomedical signal processing and control · 2023Article
- Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges.Bioengineering (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
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- 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
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10 authors.
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Abstract
This paper focuses on the study of Coronavirus Disease 2019 (COVID-19) X-ray image segmentation technology. We present a new multilevel image segmentation method based on the swarm intelligence algorithm (SIA) to enhance the image segmentation of COVID-19 X-rays. This paper first introduces an improved ant colony optimization algorithm, and later details the directional crossover (DX) and directional mutation (DM) strategy, XMACO. The DX strategy improves the quality of the population search, which enhances the convergence speed of the algorithm. The DM strategy increases the diversity of the population to jump out of the local optima (LO). Furthermore, we design the image segmentation model (MIS-XMACO) by incorporating two-dimensional (2D) histograms, 2D Kapur's entropy, and a nonlocal mean strategy, and we apply this model to COVID-19 X-ray image segmentation. Benchmark function experiments based on the IEEE CEC2014 and IEEE CEC2017 function sets demonstrate that XMACO has a faster convergence speed and higher convergence accuracy than competing models, and it can avoid falling into LO. Other SIAs and image segmentation models were used to ensure the validity of the experiments. The proposed MIS-XMACO model shows more stable and superior segmentation results than other models at different threshold levels by analyzing the experimental results.
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