Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol.This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately.Methods We propose the ...Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol.This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately.Methods We propose the VGG-Inspired stochastic pooling neural network(VISPNN)model based on three components:(i)a VGG-inspired mainstay network,(ii)the stochastic pooling technique,which aims to outperform traditional max pooling and average pooling,and(iii)an improved 20-way data augmentation(Gaussian noise,salt-and-pepper noise,speckle noise,Poisson noise,horizontal shear,vertical shear,rotation,Gamma correction,random translation,and scaling on both raw image and its horizontally mirrored image).In addition,two networks(Net-I and Net-II)are proposed in ablation studies.Net-I is based on VISPNN by replacing stochastic pooling with ordinary max pooling.Net-II removes the 20-way data augmentation.Results The results by ten runs of 10-fold cross-validation show that our VISPNN model gains a sensitivity of 97.98±1.32,a specificity of 97.80±1.35,a precision of 97.78±1.35,an accuracy of 97.89±1.11,an F1 score of 97.87±1.12,an MCC of 95.79±2.22,an FMI of 97.88±1.12,and an AUC of 0.9849,respectively.Conclusion The performance of our VISPNN model is better than two internal networks(Net-I and Net-II)and ten state-of-the-art alcoholism recognition methods.展开更多
(Aim)COVID-19 is an ongoing infectious disease.It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021.Traditional computer vision methods have achieved promising results on the automatic s...(Aim)COVID-19 is an ongoing infectious disease.It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021.Traditional computer vision methods have achieved promising results on the automatic smart diagnosis.(Method)This study aims to propose a novel deep learning method that can obtain better performance.We use the pseudo-Zernike moment(PZM),derived from Zernike moment,as the extracted features.Two settings are introducing:(i)image plane over unit circle;and(ii)image plane inside the unit circle.Afterward,we use a deep-stacked sparse autoencoder(DSSAE)as the classifier.Besides,multiple-way data augmentation is chosen to overcome overfitting.The multiple-way data augmentation is based on Gaussian noise,salt-and-pepper noise,speckle noise,horizontal and vertical shear,rotation,Gamma correction,random translation and scaling.(Results)10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06%±1.54%,a specificity of 92.56%±1.06%,a precision of 92.53%±1.03%,and an accuracy of 92.31%±1.08%.Its F1 score,MCC,and FMI arrive at 92.29%±1.10%,84.64%±2.15%,and 92.29%±1.10%,respectively.The AUC of our model is 0.9576.(Conclusion)We demonstrate“image plane over unit circle”can get better results than“image plane inside a unit circle.”Besides,this proposed PZM-DSSAE model is better than eight state-of-the-art approaches.展开更多
基金This paper is partially supported by the Royal Society International Exchanges Cost Share Award,UK(RP202G0230)Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)+3 种基金Hope Foundation for Cancer Research,UK(RM60G0680)British Heart Foundation Accelerator Award,UKSino-UK Industrial Fund,UK(RP202G0289)Global Challenges Research Fund(GCRF),UK(P202PF11).In addition,we acknowledge the help of Dr.Hemil Patel and Dr.Qinghua Zhou for their help in English correction.
文摘Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol.This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately.Methods We propose the VGG-Inspired stochastic pooling neural network(VISPNN)model based on three components:(i)a VGG-inspired mainstay network,(ii)the stochastic pooling technique,which aims to outperform traditional max pooling and average pooling,and(iii)an improved 20-way data augmentation(Gaussian noise,salt-and-pepper noise,speckle noise,Poisson noise,horizontal shear,vertical shear,rotation,Gamma correction,random translation,and scaling on both raw image and its horizontally mirrored image).In addition,two networks(Net-I and Net-II)are proposed in ablation studies.Net-I is based on VISPNN by replacing stochastic pooling with ordinary max pooling.Net-II removes the 20-way data augmentation.Results The results by ten runs of 10-fold cross-validation show that our VISPNN model gains a sensitivity of 97.98±1.32,a specificity of 97.80±1.35,a precision of 97.78±1.35,an accuracy of 97.89±1.11,an F1 score of 97.87±1.12,an MCC of 95.79±2.22,an FMI of 97.88±1.12,and an AUC of 0.9849,respectively.Conclusion The performance of our VISPNN model is better than two internal networks(Net-I and Net-II)and ten state-of-the-art alcoholism recognition methods.
基金This study was supported by Royal Society International Exchanges Cost Share Award,UK(RP202G0230)Medical Research Council Confidence in Concept Award,UK(MC_PC_17171)+1 种基金Hope Foundation for Cancer Research,UK(RM60G0680)Global Challenges Research Fund(GCRF),UK(P202PF11)。
文摘(Aim)COVID-19 is an ongoing infectious disease.It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021.Traditional computer vision methods have achieved promising results on the automatic smart diagnosis.(Method)This study aims to propose a novel deep learning method that can obtain better performance.We use the pseudo-Zernike moment(PZM),derived from Zernike moment,as the extracted features.Two settings are introducing:(i)image plane over unit circle;and(ii)image plane inside the unit circle.Afterward,we use a deep-stacked sparse autoencoder(DSSAE)as the classifier.Besides,multiple-way data augmentation is chosen to overcome overfitting.The multiple-way data augmentation is based on Gaussian noise,salt-and-pepper noise,speckle noise,horizontal and vertical shear,rotation,Gamma correction,random translation and scaling.(Results)10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06%±1.54%,a specificity of 92.56%±1.06%,a precision of 92.53%±1.03%,and an accuracy of 92.31%±1.08%.Its F1 score,MCC,and FMI arrive at 92.29%±1.10%,84.64%±2.15%,and 92.29%±1.10%,respectively.The AUC of our model is 0.9576.(Conclusion)We demonstrate“image plane over unit circle”can get better results than“image plane inside a unit circle.”Besides,this proposed PZM-DSSAE model is better than eight state-of-the-art approaches.
