Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL...Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.展开更多
Objectives: To assess the patients and health personnel’s level of awareness on risks related to ionizing radiation during CT scan. Materials and methods: Three questionnaires were addressed to patients, prescribing ...Objectives: To assess the patients and health personnel’s level of awareness on risks related to ionizing radiation during CT scan. Materials and methods: Three questionnaires were addressed to patients, prescribing physicians, and the medical imaging staff for three hospitals respectively. This permitted us to assess their knowledge on the benefits and risks of the required medical exam, based on the dangers of being exposed to X-rays, especially induced-radiation cancer following the amount of X-rays received during a CT scan and the possibility of not receiving radiation as tools of diagnosis. Results: 150 patients, 84 referring doctors of CT scan tests and 60 medical imaging personnel were retained. For patients, only 7.1% received information on the benefits and risks of their exams, and 34.4% believed that x-rays were harmful to their health. For the prescribers, 46.7% took into account the benefits/risk ratio before prescribing a test and only 16.7% of the referring doctors have informed the patient of the risks related to X-ray. 90% of the medical imaging staff ensures that the required test is justified, and 50% informed the patient on the risks associated with their radiation exposure, and the increased risk of developing cancer. 65% of the imaging staff could not estimate the dose that the patient will receive during the medical test. 25% mentioned the dose received during the acquisition in the patient’s exam report. Conclusion: This study confirms that the referring doctors, the patients, and the radiologists have a low knowledge concerning the risks associated with radiation exposure during a CT scan assessment. We will therefore say that patients and prescribers are not aware of the doses of radiation on CT and their possible risks, even though there is a risk of developing cancer.展开更多
Effective medical diagnosis is dramatically expensive,especially in third-world countries.One of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of me...Effective medical diagnosis is dramatically expensive,especially in third-world countries.One of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant challenge.Hence,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific tasks.Unfortunately,experimentation and utilization of different models of transfer learning do not achieve satisfactory results.In this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related diseases.This model is based on classification,denoising autoencoder,and transfer learning.Firstly,pre-processing is employed to prepare the medical images.It depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis process.Then,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect pneumonia.The proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray images.Finally,a comparative study is introduced for the classification performance with and without the denoising process.The proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and testing.To demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN models.The achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the existing diagnosis models.展开更多
Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic loss.But unlike smallpox,the Covid-19 recorded a low exponential ...Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic loss.But unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and diagnostics.Data analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported patients.This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques.Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task.We used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 split.This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.展开更多
High-performance all-solid-state lithium-ion batteries require observation,control,and optimization of the electrode structure.X-ray computational tomography(CT)is an effective nondestructive method for observing the ...High-performance all-solid-state lithium-ion batteries require observation,control,and optimization of the electrode structure.X-ray computational tomography(CT)is an effective nondestructive method for observing the electrode structure in three dimensions.However,the limited availability of synchrotron radiation CT,which offers high-resolution imaging with a high signal-to-noise ratio,makes it difficult to conduct experiments and restricts the use of X-ray CT in battery development.Conversely,laboratory CT systems are widely available,but they use X-rays emitted from a metal target,resulting in lower image quality and resolution compared with synchrotron radiation CT.This study explores a method for achieving comparable resolution in laboratory CT images of all-solid-state batteries to that of synchrotron radiation CT.Our method involves using the synchrotron radiation CT images as training data for machine learning super-resolution.The results demonstrate that,by employing an appropriate machine learning algorithm and activation function,along with a sufficiently deep network,the image quality of laboratory CT becomes equivalent to that of synchrotron radiation CT.展开更多
In developing countries,medical diagnosis is expensive and time consuming.Hence,automatic diagnosis can be a good cheap alternative.This task can be performed with artificial intelligence tools such as deep Convolutio...In developing countries,medical diagnosis is expensive and time consuming.Hence,automatic diagnosis can be a good cheap alternative.This task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks(CNNs).These tools can be used on medical images to speed up the diagnosis process and save the efforts of specialists.The deep CNNs allow direct learning from the medical images.However,the accessibility of classified data is still the largest challenge,particularly in the field of medical imaging.Transfer learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image classification.However,because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19,transfer learning is not usually a robust solution.Single-Image Super-Resolution(SISR)can facilitate learning to enhance computer vision functions,apart from enhancing perceptual image consistency.Consequently,it helps in showing the main features of images.Motivated by the challenging dilemma of Pneumonia and COVID-19 diagnosis,this paper introduces a hybrid CNN model,namely SIGTra,to generate super-resolution versions of X-ray and CT images.It depends on aGenerative Adversarial Network(GAN)for the super-resolution reconstruction problem.Besides,Transfer learning with CNN(TCNN)is adopted for the classification of images.Three different categories of chest X-ray and CT images can be classified with the proposed model.A comparison study is presented between the proposed SIGTra model and the other relatedCNNmodels for COVID-19 detection in terms of precision,sensitivity,and accuracy.展开更多
Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT- PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medic...Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT- PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images.Methods: A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion criteria.Results: In this survey, we reviewed 98 articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect COVID-19.Conclusions: This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.41877267 and 41877260)the Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA13010201).
文摘Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.
文摘Objectives: To assess the patients and health personnel’s level of awareness on risks related to ionizing radiation during CT scan. Materials and methods: Three questionnaires were addressed to patients, prescribing physicians, and the medical imaging staff for three hospitals respectively. This permitted us to assess their knowledge on the benefits and risks of the required medical exam, based on the dangers of being exposed to X-rays, especially induced-radiation cancer following the amount of X-rays received during a CT scan and the possibility of not receiving radiation as tools of diagnosis. Results: 150 patients, 84 referring doctors of CT scan tests and 60 medical imaging personnel were retained. For patients, only 7.1% received information on the benefits and risks of their exams, and 34.4% believed that x-rays were harmful to their health. For the prescribers, 46.7% took into account the benefits/risk ratio before prescribing a test and only 16.7% of the referring doctors have informed the patient of the risks related to X-ray. 90% of the medical imaging staff ensures that the required test is justified, and 50% informed the patient on the risks associated with their radiation exposure, and the increased risk of developing cancer. 65% of the imaging staff could not estimate the dose that the patient will receive during the medical test. 25% mentioned the dose received during the acquisition in the patient’s exam report. Conclusion: This study confirms that the referring doctors, the patients, and the radiologists have a low knowledge concerning the risks associated with radiation exposure during a CT scan assessment. We will therefore say that patients and prescribers are not aware of the doses of radiation on CT and their possible risks, even though there is a risk of developing cancer.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Effective medical diagnosis is dramatically expensive,especially in third-world countries.One of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant challenge.Hence,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific tasks.Unfortunately,experimentation and utilization of different models of transfer learning do not achieve satisfactory results.In this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related diseases.This model is based on classification,denoising autoencoder,and transfer learning.Firstly,pre-processing is employed to prepare the medical images.It depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis process.Then,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect pneumonia.The proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray images.Finally,a comparative study is introduced for the classification performance with and without the denoising process.The proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and testing.To demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN models.The achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the existing diagnosis models.
文摘Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic loss.But unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and diagnostics.Data analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported patients.This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques.Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task.We used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 split.This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.
基金The synchrotron radiation measurements were performed at BL20XU at SPring-8,with the approval of the Japan Syn-chrotron Radiation Research Institute(JASRI,proposal numbers 2022B1020,2022A1003,2021B1005,2021B1004,2021A1017,2020A1782).
文摘High-performance all-solid-state lithium-ion batteries require observation,control,and optimization of the electrode structure.X-ray computational tomography(CT)is an effective nondestructive method for observing the electrode structure in three dimensions.However,the limited availability of synchrotron radiation CT,which offers high-resolution imaging with a high signal-to-noise ratio,makes it difficult to conduct experiments and restricts the use of X-ray CT in battery development.Conversely,laboratory CT systems are widely available,but they use X-rays emitted from a metal target,resulting in lower image quality and resolution compared with synchrotron radiation CT.This study explores a method for achieving comparable resolution in laboratory CT images of all-solid-state batteries to that of synchrotron radiation CT.Our method involves using the synchrotron radiation CT images as training data for machine learning super-resolution.The results demonstrate that,by employing an appropriate machine learning algorithm and activation function,along with a sufficiently deep network,the image quality of laboratory CT becomes equivalent to that of synchrotron radiation CT.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program.
文摘In developing countries,medical diagnosis is expensive and time consuming.Hence,automatic diagnosis can be a good cheap alternative.This task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks(CNNs).These tools can be used on medical images to speed up the diagnosis process and save the efforts of specialists.The deep CNNs allow direct learning from the medical images.However,the accessibility of classified data is still the largest challenge,particularly in the field of medical imaging.Transfer learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image classification.However,because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19,transfer learning is not usually a robust solution.Single-Image Super-Resolution(SISR)can facilitate learning to enhance computer vision functions,apart from enhancing perceptual image consistency.Consequently,it helps in showing the main features of images.Motivated by the challenging dilemma of Pneumonia and COVID-19 diagnosis,this paper introduces a hybrid CNN model,namely SIGTra,to generate super-resolution versions of X-ray and CT images.It depends on aGenerative Adversarial Network(GAN)for the super-resolution reconstruction problem.Besides,Transfer learning with CNN(TCNN)is adopted for the classification of images.Three different categories of chest X-ray and CT images can be classified with the proposed model.A comparison study is presented between the proposed SIGTra model and the other relatedCNNmodels for COVID-19 detection in terms of precision,sensitivity,and accuracy.
文摘Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT- PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images.Methods: A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion criteria.Results: In this survey, we reviewed 98 articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect COVID-19.Conclusions: This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.
