To solve the problems of complex lesion region morphology,blurred edges,and limited hardware resources for deploying the recognition model in pneumonia image recognition,an improved EfficientNetV2 pneumo-nia recogniti...To solve the problems of complex lesion region morphology,blurred edges,and limited hardware resources for deploying the recognition model in pneumonia image recognition,an improved EfficientNetV2 pneumo-nia recognition model based on multiscale attention is proposed.First,the number of main module stacks of the model is reduced to avoid overfitting,while the dilated convolution is introduced in the first convolutional layer to expand the receptive field of the model;second,a redesigned improved mobile inverted bottleneck convolution(IMBConv)module is proposed,in which GSConv is introduced to enhance the model’s attention to inter-channel information,and a SimAM module is introduced to reduce the number of model parameters while guaranteeing the model’s recognition performance;finally,an improved multi-scale efficient local attention(MELA)module is proposed to ensure the model’s recognition ability for pneumonia images with complex lesion regions.The experimental results show that the improved model has a computational complexity of 1.96 GFLOPs,which is reduced by 32%relative to the baseline model,and the number of model parameters is also reduced,and achieves an accuracy of 86.67%on the triple classification task of the public dataset Chest X-ray,representing an improvement of 2.74%compared to the baseline model.The recognition accuracies of ResNet50,Inception-V4,and Swin Transformer V2 on this dataset are 84.36%,85.98%,and 83.42%,respectively,and their computational complexities and model parameter counts are all higher than those of the proposed model.This indicates that the proposed model has very high feasibility for deployment in edge computing or mobile healthcare systems.In addition,the improved model achieved the highest accuracy of 90.98%on the four-classification public dataset compared to other models,indicating that the model has better recognition accuracy and generalization ability for pneumonia image recognition.展开更多
Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,signifi...Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,significant variability in shape and location,and low contrast with surrounding tissues,achieving high segmentation accuracy remains challenging.To improve segmentation precision,we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas fromCT images.Firstly,an EfficientNetV2 encoder is employed to extract complex and multi-level features,enhancing the model’s ability to capture the pancreas’s intricate morphology.Then,a residual multi-branch dilated attention(RMDA)module is designed to suppress irrelevant background noise and highlight useful pancreatic features.And re-parameterization Visual Geometry Group(RepVGG)blocks with amulti-branch structure are introduced in the decoder to effectively integrate deep features and low-level details,improving segmentation accuracy.Furthermore,we apply re-parameterization to the model,reducing computations and parameters while accelerating inference and reducing memory usage.Our approach achieves average dice similarity coefficient(DSC)of 85.59%,intersection over union(IoU)of 75.03%,precision of 85.09%,and recall of 86.57%on the NIH pancreas dataset.Compared with other methods,our model has fewer parameters and faster inference speed,demonstrating its enormous potential in practical applications of pancreatic segmentation.展开更多
The unique properties of TiO_(2)-sulfur(TiO_(2)-S)modified graphene nanocomposite electrode(GPE/TiO_(2)-S)in the electrochemical sensing of formaldehyde compound has been evaluated.We prepared TiO_(2)-S by hydrotherma...The unique properties of TiO_(2)-sulfur(TiO_(2)-S)modified graphene nanocomposite electrode(GPE/TiO_(2)-S)in the electrochemical sensing of formaldehyde compound has been evaluated.We prepared TiO_(2)-S by hydrothermal method and modified the graphene nanocomposite electrode by applying electrochemical cyclic voltammetry(CV)approach.The TiO_(2)-S nanocomposite was characterized by X-ray diffraction(XRD),while the GPE/TiO_(2)-S was examined by scanning electron microscopy(FESEM)and X-Ray fluorosense(XRF)techniques.TiO_(2)-S has a grain size of 19.32 nm.The surface morphology of the GPE/TiO_(2)-S nanocomposite shows a good,intact,and tightly porous structure with TiO_(2)-S covers the graphene surface.The content of optimized GPE/TiO_(2)-S electrodes is 41.5%of graphene,37.8%of TiO_(2),and 12.4%of sulfur that was prepared by mixing 1 g of TiO_(2)-S with 0.5 g of graphene and 0.3 mL paraffin.The GPE/TiO_(2)-S electrode produces a high anodic current(I_(pa))of 800μA and a high cathodic current(I_(pc))of-600μA at a scan rate of 0.1 V·s^(-1)using an electrolyte0.01 mol·L^(-1)K_3[Fe(CN)_6]solution containing 150 mg·L^(-1)formaldehyde.The limit of detection can reach as low as 9.7 mg·L^(-1)with stability with Horwitz ratio value as low as 0.397.The composite electrode also exhibits excellent slectivity properties by showing clear formaldehyde sugnal in the presence of high concentration of interfering agent.GPE/TiO_(2)-S electrode should find potential application of formaldehyde detection in food industries.展开更多
基金supported by the Scientific Research Fund of Hunan Provincial Education Department,China(Grant Nos.21C0439,22A0408).
文摘To solve the problems of complex lesion region morphology,blurred edges,and limited hardware resources for deploying the recognition model in pneumonia image recognition,an improved EfficientNetV2 pneumo-nia recognition model based on multiscale attention is proposed.First,the number of main module stacks of the model is reduced to avoid overfitting,while the dilated convolution is introduced in the first convolutional layer to expand the receptive field of the model;second,a redesigned improved mobile inverted bottleneck convolution(IMBConv)module is proposed,in which GSConv is introduced to enhance the model’s attention to inter-channel information,and a SimAM module is introduced to reduce the number of model parameters while guaranteeing the model’s recognition performance;finally,an improved multi-scale efficient local attention(MELA)module is proposed to ensure the model’s recognition ability for pneumonia images with complex lesion regions.The experimental results show that the improved model has a computational complexity of 1.96 GFLOPs,which is reduced by 32%relative to the baseline model,and the number of model parameters is also reduced,and achieves an accuracy of 86.67%on the triple classification task of the public dataset Chest X-ray,representing an improvement of 2.74%compared to the baseline model.The recognition accuracies of ResNet50,Inception-V4,and Swin Transformer V2 on this dataset are 84.36%,85.98%,and 83.42%,respectively,and their computational complexities and model parameter counts are all higher than those of the proposed model.This indicates that the proposed model has very high feasibility for deployment in edge computing or mobile healthcare systems.In addition,the improved model achieved the highest accuracy of 90.98%on the four-classification public dataset compared to other models,indicating that the model has better recognition accuracy and generalization ability for pneumonia image recognition.
基金supported by the Science and Technology Innovation Programof Hunan Province(Grant No.2022RC1021)the Hunan Provincial Natural Science Foundation Project(Grant No.2023JJ60124)+1 种基金the Changsha Natural Science Foundation Project(Grant No.kq2202265)the key project of the Hunan Provincial of Education(Grant No.22A0255).
文摘Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,significant variability in shape and location,and low contrast with surrounding tissues,achieving high segmentation accuracy remains challenging.To improve segmentation precision,we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas fromCT images.Firstly,an EfficientNetV2 encoder is employed to extract complex and multi-level features,enhancing the model’s ability to capture the pancreas’s intricate morphology.Then,a residual multi-branch dilated attention(RMDA)module is designed to suppress irrelevant background noise and highlight useful pancreatic features.And re-parameterization Visual Geometry Group(RepVGG)blocks with amulti-branch structure are introduced in the decoder to effectively integrate deep features and low-level details,improving segmentation accuracy.Furthermore,we apply re-parameterization to the model,reducing computations and parameters while accelerating inference and reducing memory usage.Our approach achieves average dice similarity coefficient(DSC)of 85.59%,intersection over union(IoU)of 75.03%,precision of 85.09%,and recall of 86.57%on the NIH pancreas dataset.Compared with other methods,our model has fewer parameters and faster inference speed,demonstrating its enormous potential in practical applications of pancreatic segmentation.
基金the financial support from the Ministry of Education,Culture,Research and Technology of the Republic of Indonesia under the Applied Research award(DIPA023.17.1.690523/2023)the World Class Professor award grant 2023。
文摘The unique properties of TiO_(2)-sulfur(TiO_(2)-S)modified graphene nanocomposite electrode(GPE/TiO_(2)-S)in the electrochemical sensing of formaldehyde compound has been evaluated.We prepared TiO_(2)-S by hydrothermal method and modified the graphene nanocomposite electrode by applying electrochemical cyclic voltammetry(CV)approach.The TiO_(2)-S nanocomposite was characterized by X-ray diffraction(XRD),while the GPE/TiO_(2)-S was examined by scanning electron microscopy(FESEM)and X-Ray fluorosense(XRF)techniques.TiO_(2)-S has a grain size of 19.32 nm.The surface morphology of the GPE/TiO_(2)-S nanocomposite shows a good,intact,and tightly porous structure with TiO_(2)-S covers the graphene surface.The content of optimized GPE/TiO_(2)-S electrodes is 41.5%of graphene,37.8%of TiO_(2),and 12.4%of sulfur that was prepared by mixing 1 g of TiO_(2)-S with 0.5 g of graphene and 0.3 mL paraffin.The GPE/TiO_(2)-S electrode produces a high anodic current(I_(pa))of 800μA and a high cathodic current(I_(pc))of-600μA at a scan rate of 0.1 V·s^(-1)using an electrolyte0.01 mol·L^(-1)K_3[Fe(CN)_6]solution containing 150 mg·L^(-1)formaldehyde.The limit of detection can reach as low as 9.7 mg·L^(-1)with stability with Horwitz ratio value as low as 0.397.The composite electrode also exhibits excellent slectivity properties by showing clear formaldehyde sugnal in the presence of high concentration of interfering agent.GPE/TiO_(2)-S electrode should find potential application of formaldehyde detection in food industries.
