(Aim)The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022.Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients.(Method)Two datasets are ch...(Aim)The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022.Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients.(Method)Two datasets are chosen for this study.The multiple-way data augmentation,including speckle noise,random translation,scaling,salt-and-pepper noise,vertical shear,Gamma correction,rotation,Gaussian noise,and horizontal shear,is harnessed to increase the size of the training set.Then,the SqueezeNet(SN)with complex bypass is used to generate SN features.Finally,the extreme learning machine(ELM)is used to serve as the classifier due to its simplicity of usage,quick learning speed,and great generalization performances.The number of hidden neurons in ELM is set to 2000.Ten runs of 10-fold cross-validation are implemented to generate impartial results.(Result)For the 296-image dataset,our SNELM model attains a sensitivity of 96.35±1.50%,a specificity of 96.08±1.05%,a precision of 96.10±1.00%,and an accuracy of 96.22±0.94%.For the 640-image dataset,the SNELM attains a sensitivity of 96.00±1.25%,a specificity of 96.28±1.16%,a precision of 96.28±1.13%,and an accuracy of 96.14±0.96%.(Conclusion)The proposed SNELM model is successful in diagnosing COVID-19.The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.展开更多
基金This paper is partially supported by Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)Royal Society International Exchanges Cost Share Award,UK(RP202G0230)+5 种基金British Heart Foundation Accelerator Award,UK(AA/18/3/34220)Hope Foundation for Cancer Research,UK(RM60G0680)Global Challenges Research Fund(GCRF),UK(P202PF11)Sino-UK Industrial Fund,UK(RP202G0289)LIAS Pioneering Partnerships award,UK(P202ED10)Data Science Enhancement Fund,UK(P202RE237).
文摘(Aim)The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022.Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients.(Method)Two datasets are chosen for this study.The multiple-way data augmentation,including speckle noise,random translation,scaling,salt-and-pepper noise,vertical shear,Gamma correction,rotation,Gaussian noise,and horizontal shear,is harnessed to increase the size of the training set.Then,the SqueezeNet(SN)with complex bypass is used to generate SN features.Finally,the extreme learning machine(ELM)is used to serve as the classifier due to its simplicity of usage,quick learning speed,and great generalization performances.The number of hidden neurons in ELM is set to 2000.Ten runs of 10-fold cross-validation are implemented to generate impartial results.(Result)For the 296-image dataset,our SNELM model attains a sensitivity of 96.35±1.50%,a specificity of 96.08±1.05%,a precision of 96.10±1.00%,and an accuracy of 96.22±0.94%.For the 640-image dataset,the SNELM attains a sensitivity of 96.00±1.25%,a specificity of 96.28±1.16%,a precision of 96.28±1.13%,and an accuracy of 96.14±0.96%.(Conclusion)The proposed SNELM model is successful in diagnosing COVID-19.The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.
文摘目的探讨升-降主动脉人工血管旁路术治疗成人复杂主动脉缩窄的手术效果并总结其临床经验.方法 2015年10月至2018年7月同济大学附属东方医院心外科应用升主动脉-降主动脉人工血管转流术治疗成人复杂主动脉缩窄2例,均为男性,年龄分别为22岁和46岁.两例患者均经桡动脉、足背动脉穿刺测压,根据桡动脉和足背动脉手术前后平均压差变化评价手术效果.结果术后均治愈出院.术前桡动脉和足背动脉平均压差分别为48 mmHg和55 mm Hg;术后桡动脉和足背动脉平均压差6mmHg和9mmHg,较术前明显缩小.术后主动脉CTA复查示转流人工血管通畅.结论升-降主动脉人工血管旁路术是治疗成人复杂主动脉缩窄的有效手段.
