Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

Su, Hang; Zhao, Dong; Elmannai, Hela; Heidari, Ali Asghar; Bourouis, Sami; Wu, Zongda; Cai, Zhennao; Gui, Wenyong; Chen, Mayun

doi:10.1016/j.compbiomed.2022.105618

Cited by 129 publications

(26 citation statements)

References 151 publications

(151 reference statements)

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“…Lu [ 55 ] applied the neural network to recognize the endoscopic image of upper digestive tract, by regarding 1335 cases of digestive tract endoscopic images as the dataset, and achieve an accuracy rate of 94.20%. Su et al [ 56 ] put forward a multi-level thresholding image segmentation method, which introduces horizontal and vertical search mechanisms into Multi-Verse Optimizer, and achieves the improvement of global search and the ability to jump out of local optimum, but it is more time-consuming and ignores classification and prediction of lesions. Therefore, Ieracitano et al [ 57 ] integrated CXR images with fuzzy features to overcome the uncertainty of CXR edge images, achieving COVID-19 classification accuracy rate of 81%.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Liu

Cai

Tang

et al. 2022

Computers in Biology and Medicine

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Section: Related Workmentioning

confidence: 99%

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Liu

Cai

Tang

et al. 2022

Computers in Biology and Medicine

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“…In recent years, artificial intelligence has played an important role in the medical field, such as coronavirus disease 2019 (COVID-19) diagnosis [ 4 ], detection of gastrointestinal polyps [ 5 ], retinal vessel segmentation [ 6 ], image diagnosis of lung cancer [ 7 ], diagnosis of atrophic gastritis [ 8 ], and confidentiality management of electronic medical records on the cloud [ 9 ]. Machine learning, a form of artificial intelligence, combined with medical big data can create algorithms that rival those of human doctors [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Construct and Validate a Predictive Model for Surgical Site Infection after Posterior Lumbar Interbody Fusion Based on Machine Learning Algorithm

Xiong

Zhao

et al. 2022

Computational and Mathematical Methods in Medicine

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Purpose. Surgical site infection is one of the serious complications after lumbar fusion. Early prediction and timely intervention can reduce the harm to patients. The aims of this study were to construct and validate a machine learning model for predicting surgical site infection after posterior lumbar interbody fusion, to screen out the most important risk factors for surgical site infection, and to explore whether synthetic minority oversampling technique could improve the model performance. Method. This study reviewed 584 patients who underwent posterior lumbar interbody fusion for degenerative lumbar disease at our center from January 2019 to August 2021. Clinical information and laboratory test data were collected from the electronic medical records. The original dataset was divided into training set and validation set in a 1 : 1 ratio. Seven machine learning algorithms were used to develop predictive models; the training set of each model was resampled using synthetic minority oversampling technique. Finally, the model performance was assessed in the validation set. Results. Of the 584 patients, 33 (5.65%) occurred surgical site infection. Stepwise logistic regression showed that preoperative albumin level (OR 0.659, 95% CI 0.563-0.756), diabetes (OR 9.129, 95% CI 3.816-23.126), intraoperative dural tear (OR 8.436, 95% CI 2.729-25.334), and rheumatic disease (OR 8.471, 95% CI 1.743-39.567) were significant predictors associated with surgical site infection. The performance of the AdaBoost Classification Trees model was the best among the seven machine learning models, and synthetic minority oversampling technique improved the performance of all models. Conclusion. The prediction model we constructed based on machine learning and synthetic minority oversampling technique can accurately predict surgical site infection, which is conducive to clinical decision-making and optimization of perioperative management.

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“…Several related works provide powerful boost to medical research. Su et al [ 57 ] reported a framework using horizontal and vertical multiverse optimization, providing an effective segmentation method for diagnosing Coronavirus Disease 2019 (COVID-19). Similarly, Qi et al [ 58 ] reported a directional mutation and crossover boosted ant colony optimization for diagnosing COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Identification of the Potential Molecular Mechanism of TGFBI Gene in Persistent Atrial Fibrillation

Guan

Liu

Huang

et al. 2022

Computational and Mathematical Methods in Medicine

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Background. Transforming growth factor beta-induced protein (TGFBI, encoded by TGFBI gene), is an extracellular matrix protein, widely expressed in variety of tissues. It binds to collagens type I, II, and IV and plays important roles in the interactions of cell with cell, collagen, and matrix. It has been reported to be associated with myocardial fibrosis, and the latter is an important pathophysiologyical basis of atrial fibrillation (AF). However, the mechanism of TGFBI in AF remains unclear. We aimed to detect the potential mechanism of TGFBI in AF via bioinformatics analysis. Methods. The microarray dataset of GSE115574 was examined to detect the genes coexpressed with TGFBI from 14 left atrial tissue samples of AF patients. TGFBI coexpression genes were then screened using the R package. Using online analytical tools, we determined the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) network of TGFBI and its coexpression genes. The modules and hub genes of the PPI-network were then identified. Another dataset, GSE79768 was examined to verify the hub genes. DrugBank was used to detect the potential target drugs. Results. In GSE115574 dataset, a total of 1818 coexpression genes (769 positive and 1049 negative) were identified, enriched in 120 biological processes (BP), 38 cellular components (CC), 36 molecular functions (MF), and 39 KEGG pathways. A PPI-network with average 12.2-degree nodes was constructed. The genes clustered in the top module constructed from this network mainly play a role in PI3K-Akt signaling pathway, viral myocarditis, inflammatory bowel disease, and platelet activation. CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 were identified and finally verified as the hub genes, mainly enriched in pathways like leukocyte transendothelial migration, PI3K-Akt signaling pathway, viral myocarditis, rheumatoid arthritis, and platelet activation. Pegcetacoplan, ocriplasmin, and carvedilol were the potential target drugs. Conclusions. We used microdataset to identify the potential functions and mechanisms of the TGFBI and its coexpression genes in AF patients. Our findings suggest that CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 may be the hub genes.

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Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

Cited by 129 publications

References 151 publications

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Construct and Validate a Predictive Model for Surgical Site Infection after Posterior Lumbar Interbody Fusion Based on Machine Learning Algorithm

Identification of the Potential Molecular Mechanism of TGFBI Gene in Persistent Atrial Fibrillation

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