Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational compl...Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues.This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication.The proposed model has two stages of learning;the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model.The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity.The proposed model achieves robust and reliable end-to-end Palmprint authentication by extracting the convolutional features using VGG-16 Palmprint and the similarity of two input Palmprint using the Siamese network.The second stage uses the CASIA dataset to train and test the Siamese network.The suggested model outperforms comparable studies based on the deep learning approach achieving accuracy and EER of 91.8%and 0.082%,respectively,on the CASIA left-hand images and accuracy and EER of 91.7%and 0.084,respectively,on the CASIA right-hand images.展开更多
The convolutional neural network(CNN)is one of the main algorithms that is applied to deep transfer learning for classifying two essential types of liver lesions;Hemangioma and hepatocellular carcinoma(HCC).Ultrasound...The convolutional neural network(CNN)is one of the main algorithms that is applied to deep transfer learning for classifying two essential types of liver lesions;Hemangioma and hepatocellular carcinoma(HCC).Ultrasound images,which are commonly available and have low cost and low risk compared to computerized tomography(CT)scan images,will be used as input for the model.A total of 350 ultrasound images belonging to 59 patients are used.The number of images with HCC is 202 and 148,respectively.These images were collected from ultrasound cases.info(28 Hemangiomas patients and 11 HCC patients),the department of radiology,the University of Washington(7 HCC patients),the Atlas of ultrasound Germany(3 HCC patients),and Radiopedia and others(10 HCC patients).The ultrasound images are divided into 225,52,and 73 for training,validation,and testing.A data augmentation technique is used to enhance the validation performance.We proposed an approach based on ensembles of the best-selected deep transfer models from the on-the-shelf models:VGG16,VGG19,DenseNet,Inception,InceptionResNet,ResNet,and EfficientNet.After tuning both the feature extraction and the classification layers,the best models are selected.Validation accuracy is used for model tuning and selection.The accuracy,sensitivity,specificity and AUROC are used to evaluate the performance.The experiments are concluded in five stages.The first stage aims to evaluate the base model performance by training the on-the-shelf models.The best accu-racy obtained in the first stage is 83.5%.In the second stage,we augmented the data and retrained the on-the-shelf models with the augmented data.The best accuracy we obtained in the second stage was 86.3%.In the third stage,we tuned the feature extraction layers of the on-the-shelf models.The best accuracy obtained in the third stage is 89%.In the fourth stage,we fine-tuned the classification layer and obtained an accuracy of 93%as the best accuracy.In the fifth stage,we applied the ensemble approach using the best three-performing models and obtained an accuracy,specificity,sensitivity,and AUROC of 94%,93.7%,95.1%,and 0.944,respectively.展开更多
Many types of research focus on utilizing Palmprint recognition in user identification and authentication.The Palmprint is one of biometric authentication(something you are)invariable during a person’s life and needs...Many types of research focus on utilizing Palmprint recognition in user identification and authentication.The Palmprint is one of biometric authentication(something you are)invariable during a person’s life and needs careful protection during enrollment into different biometric authentication systems.Accuracy and irreversibility are critical requirements for securing the Palmprint template during enrollment and verification.This paper proposes an innovative HAMTE neural network model that contains Hetero-Associative Memory for Palmprint template translation and projection using matrix multiplication and dot product multiplication.A HAMTE-Siamese network is constructed,which accepts two Palmprint templates and predicts whether these two templates belong to the same user or different users.The HAMTE is generated for each user during the enrollment phase,which is responsible for generating a secure template for the enrolled user.The proposed network secures the person’s Palmprint template by translating it into an irreversible template(different features space).It can be stored safely in a trusted/untrusted third-party authentication system that protects the original person’s template from being stolen.Experimental results are conducted on the CASIA database,where the proposed network achieved accuracy close to the original accuracy for the unprotected Palmprint templates.The recognition accuracy deviated by around 3%,and the equal error rate(EER)by approximately 0.02 compared to the original data,with appropriate performance(approximately 13 ms)while preserving the irreversibility property of the secure template.Moreover,the brute-force attack has been analyzed under the new Palmprint protection scheme.展开更多
It is important to determine early on which patients require ICU admissions in managing COVID-19 especially when medical resources are limited.Delay in ICU admissions is associated with negative outcomes such as morta...It is important to determine early on which patients require ICU admissions in managing COVID-19 especially when medical resources are limited.Delay in ICU admissions is associated with negative outcomes such as mortality and cost.Therefore,early identification of patients with a high risk of respiratory failure can prevent complications,enhance risk stratification,and improve the outcomes of severely-ill hospitalized patients.In this paper,we develop a model that uses the characteristics and information collected at the time of patients’admissions and during their early period of hospitalization to accurately predict whether they will need ICU admissions.We use the data explained and organized in a window-based manner by the Sírio-Libanês hospital team(published on Kaggle).Preprocessing is applied,including imputation,cleaning,and feature selection.In the cleaning process,we remove zero-variance,redundant,and/or highly correlated(measured by the Pearson correlation coefficient)features.We use Extreme Gradient Boosting(XGBoost)with early stopping as a predictor in our developed model.We run the experiment in four stages starting from the features of Window 1 in Stage 1 and then incrementally add the features of Windows 2–4 in Stages 2–4 respectively.We achieve AUCs of 0.73,0.92,0.95,and 0.97 in those four stages.展开更多
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant No.(DSR-2022-RG-0104).
文摘Palmprint identification has been conducted over the last two decades in many biometric systems.High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues.This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication.The proposed model has two stages of learning;the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model.The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity.The proposed model achieves robust and reliable end-to-end Palmprint authentication by extracting the convolutional features using VGG-16 Palmprint and the similarity of two input Palmprint using the Siamese network.The second stage uses the CASIA dataset to train and test the Siamese network.The suggested model outperforms comparable studies based on the deep learning approach achieving accuracy and EER of 91.8%and 0.082%,respectively,on the CASIA left-hand images and accuracy and EER of 91.7%and 0.084,respectively,on the CASIA right-hand images.
基金funded by the Deanship of Scientific Research at Jouf University under Grant No.(DSR-2022-RG-0104).
文摘The convolutional neural network(CNN)is one of the main algorithms that is applied to deep transfer learning for classifying two essential types of liver lesions;Hemangioma and hepatocellular carcinoma(HCC).Ultrasound images,which are commonly available and have low cost and low risk compared to computerized tomography(CT)scan images,will be used as input for the model.A total of 350 ultrasound images belonging to 59 patients are used.The number of images with HCC is 202 and 148,respectively.These images were collected from ultrasound cases.info(28 Hemangiomas patients and 11 HCC patients),the department of radiology,the University of Washington(7 HCC patients),the Atlas of ultrasound Germany(3 HCC patients),and Radiopedia and others(10 HCC patients).The ultrasound images are divided into 225,52,and 73 for training,validation,and testing.A data augmentation technique is used to enhance the validation performance.We proposed an approach based on ensembles of the best-selected deep transfer models from the on-the-shelf models:VGG16,VGG19,DenseNet,Inception,InceptionResNet,ResNet,and EfficientNet.After tuning both the feature extraction and the classification layers,the best models are selected.Validation accuracy is used for model tuning and selection.The accuracy,sensitivity,specificity and AUROC are used to evaluate the performance.The experiments are concluded in five stages.The first stage aims to evaluate the base model performance by training the on-the-shelf models.The best accu-racy obtained in the first stage is 83.5%.In the second stage,we augmented the data and retrained the on-the-shelf models with the augmented data.The best accuracy we obtained in the second stage was 86.3%.In the third stage,we tuned the feature extraction layers of the on-the-shelf models.The best accuracy obtained in the third stage is 89%.In the fourth stage,we fine-tuned the classification layer and obtained an accuracy of 93%as the best accuracy.In the fifth stage,we applied the ensemble approach using the best three-performing models and obtained an accuracy,specificity,sensitivity,and AUROC of 94%,93.7%,95.1%,and 0.944,respectively.
基金This work was funded by the Deanship of Scientific Research at Jouf University under Grant No.(DSR-2022-RG-0104).
文摘Many types of research focus on utilizing Palmprint recognition in user identification and authentication.The Palmprint is one of biometric authentication(something you are)invariable during a person’s life and needs careful protection during enrollment into different biometric authentication systems.Accuracy and irreversibility are critical requirements for securing the Palmprint template during enrollment and verification.This paper proposes an innovative HAMTE neural network model that contains Hetero-Associative Memory for Palmprint template translation and projection using matrix multiplication and dot product multiplication.A HAMTE-Siamese network is constructed,which accepts two Palmprint templates and predicts whether these two templates belong to the same user or different users.The HAMTE is generated for each user during the enrollment phase,which is responsible for generating a secure template for the enrolled user.The proposed network secures the person’s Palmprint template by translating it into an irreversible template(different features space).It can be stored safely in a trusted/untrusted third-party authentication system that protects the original person’s template from being stolen.Experimental results are conducted on the CASIA database,where the proposed network achieved accuracy close to the original accuracy for the unprotected Palmprint templates.The recognition accuracy deviated by around 3%,and the equal error rate(EER)by approximately 0.02 compared to the original data,with appropriate performance(approximately 13 ms)while preserving the irreversibility property of the secure template.Moreover,the brute-force attack has been analyzed under the new Palmprint protection scheme.
基金This work is supported by the Deanship of Scientific Research at Jouf University under Grant No.(CV-33-41).
文摘It is important to determine early on which patients require ICU admissions in managing COVID-19 especially when medical resources are limited.Delay in ICU admissions is associated with negative outcomes such as mortality and cost.Therefore,early identification of patients with a high risk of respiratory failure can prevent complications,enhance risk stratification,and improve the outcomes of severely-ill hospitalized patients.In this paper,we develop a model that uses the characteristics and information collected at the time of patients’admissions and during their early period of hospitalization to accurately predict whether they will need ICU admissions.We use the data explained and organized in a window-based manner by the Sírio-Libanês hospital team(published on Kaggle).Preprocessing is applied,including imputation,cleaning,and feature selection.In the cleaning process,we remove zero-variance,redundant,and/or highly correlated(measured by the Pearson correlation coefficient)features.We use Extreme Gradient Boosting(XGBoost)with early stopping as a predictor in our developed model.We run the experiment in four stages starting from the features of Window 1 in Stage 1 and then incrementally add the features of Windows 2–4 in Stages 2–4 respectively.We achieve AUCs of 0.73,0.92,0.95,and 0.97 in those four stages.
