Larch wood is structurally classifi ed in many countries as one of conifers with the highest load-bearing capacity(strength class of C30).The Spanish visual classifi cation regulation only assigns a strength class to ...Larch wood is structurally classifi ed in many countries as one of conifers with the highest load-bearing capacity(strength class of C30).The Spanish visual classifi cation regulation only assigns a strength class to 4 pine woods:Laricio pine(Pinus nigra Arn.var.Salzmannii),Silvestre pine(Pinus sylvestris L.),Radiata pine(Pinus radiata D.Don),and Pinaster pine(Pinus pinaster Ait.).This work adds to the number of structurally characterised species by creating a visual classifi cation table for Japanese larch wood(Larix kaempferi(Lamb.)Carr.)which diff erentiates between 2 visual classes,MEG-1 and MEG-2.Characteristic strength values were calculated for each class(fk,MEG-1=31.80 MPa,f k,MEG-2=24.55 MPa),mean module of elasticity(E 0,mean,MEG-1=13,082 MPA,E 0,mean,MEG-2=12,320 MPA)and density(ρk,MEG-1=456.6 kg m−3,ρk,MEG-2=469.1 kg m−3),before fi nally assigning a strength class of C30 to visual class MEG-1,and a strength class of C24 to visual class MEG-2.展开更多
Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,...Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.展开更多
基金We thanks to Basque centre of research and applied innovation in vet(TKNIKA),Centre for services and promotion of Castilla y León forestry and its industry(CESEFOR),D.Bixente Dorronsoro,Gipuzkoa provincial council,and Commercial services of the wood of Guipuzkoa(SECOMA).Larranaga sawmill(Azpeitia).
文摘Larch wood is structurally classifi ed in many countries as one of conifers with the highest load-bearing capacity(strength class of C30).The Spanish visual classifi cation regulation only assigns a strength class to 4 pine woods:Laricio pine(Pinus nigra Arn.var.Salzmannii),Silvestre pine(Pinus sylvestris L.),Radiata pine(Pinus radiata D.Don),and Pinaster pine(Pinus pinaster Ait.).This work adds to the number of structurally characterised species by creating a visual classifi cation table for Japanese larch wood(Larix kaempferi(Lamb.)Carr.)which diff erentiates between 2 visual classes,MEG-1 and MEG-2.Characteristic strength values were calculated for each class(fk,MEG-1=31.80 MPa,f k,MEG-2=24.55 MPa),mean module of elasticity(E 0,mean,MEG-1=13,082 MPA,E 0,mean,MEG-2=12,320 MPA)and density(ρk,MEG-1=456.6 kg m−3,ρk,MEG-2=469.1 kg m−3),before fi nally assigning a strength class of C30 to visual class MEG-1,and a strength class of C24 to visual class MEG-2.
基金the National Natural Science Foundation of China(No.62276210,82201148,61775180)the Natural Science Basic Research Program of Shaanxi Province(No.2022JM-380)+3 种基金the Shaanxi Province College Students'Innovation and Entrepreneurship Training Program(No.S202311664128X)the Natural Science Foundation of Zhejiang Province(No.LQ22H120002)the Medical Health Science and Technology Project of Zhejiang Province(No.2022RC069,2023KY1140)the Natural Science Foundation of Ningbo(No.2023J390)。
文摘Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.
