Using restored two-dimensional X-ray images to reconstruct the three-dimensional magnetopause

Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the proposed technology may be feasible for processing SXI-simulated observation images.

Automated Algorithms for Detecting and Classifying X-Ray Images of Spine Fractures

This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spinefractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picturesegmentation, feature reduction, and image classification. Two important elements are investigated to reducethe classification time: Using feature reduction software and leveraging the capabilities of sophisticated digitalprocessing hardware. The researchers use different algorithms for picture enhancement, including theWiener andKalman filters, and they look into two background correction techniques. The article presents a technique forextracting textural features and evaluates three picture segmentation algorithms and three fractured spine detectionalgorithms using transformdomain, PowerDensity Spectrum(PDS), andHigher-Order Statistics (HOS) for featureextraction.With an emphasis on reducing digital processing time, this all-encompassing method helps to create asimplified system for classifying fractured spine fractures. A feature reduction program code has been built toimprove the processing speed for picture classification. Overall, the proposed approach shows great potential forsignificantly reducing classification time in clinical settings where time is critical. In comparison to other transformdomains, the texture features’ discrete cosine transform (DCT) yielded an exceptional classification rate, and theprocess of extracting features from the transform domain took less time. More capable hardware can also result inquicker execution times for the feature extraction algorithms.

Pulmonary Edema and Pleural Effusion Detection Using Efficient Net-V1-B4 Architecture and AdamW Optimizer from Chest X-Rays Images

This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14,PadChest,and CheXpert databases,with 10,287,6022,and 12,000 samples representing Pleural Effusion,Pulmonary Edema,and Normal cases,respectively.Consequently,the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization(CLAHE)method to boost the local contrast of the X-ray samples,then resizing the images to 380×380 dimensions,followed by using the data augmentation technique.The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer.The proposed multiclass system achieved an accuracy(ACC)of 98.3%,recall of 98.3%,precision of 98.7%,and F1-score of 98.7%.Moreover,the robustness of the model was revealed by the Receiver Operating Characteristic(ROC)analysis,which demonstrated an Area Under the Curve(AUC)of 1.00 for edema and normal cases and 0.99 for effusion.The experimental results demonstrate the superiority of the proposedmulti-class system,which has the potential to assist clinicians in timely and accurate diagnosis,leading to improved patient outcomes.Notably,ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images,which will aid clinicians in interpreting and localizing abnormalities more effectively.

The Soft X-ray Imager(SXI)on the SMILE Mission

The Soft X-ray Imager(SXI)is part of the scientific payload of the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.SMILE is a joint science mission between the European Space Agency(ESA)and the Chinese Academy of Sciences(CAS)and is due for launch in 2025.SXI is a compact X-ray telescope with a wide field-of-view(FOV)capable of encompassing large portions of Earth’s magnetosphere from the vantage point of the SMILE orbit.SXI is sensitive to the soft X-rays produced by the Solar Wind Charge eXchange(SWCX)process produced when heavy ions of solar wind origin interact with neutral particles in Earth’s exosphere.SWCX provides a mechanism for boundary detection within the magnetosphere,such as the position of Earth’s magnetopause,because the solar wind heavy ions have a very low density in regions of closed magnetic field lines.The sensitivity of the SXI is such that it can potentially track movements of the magnetopause on timescales of a few minutes and the orbit of SMILE will enable such movements to be tracked for segments lasting many hours.SXI is led by the University of Leicester in the United Kingdom(UK)with collaborating organisations on hardware,software and science support within the UK,Europe,China and the United States.

SMILE soft X-ray Imager flight model CCD370 pre-flight device characterisation

Throughout the SMILE mission the satellite will be bombarded by radiation which gradually damages the focal plane devices and degrades their performance.In order to understand the changes of the CCD370s within the soft X-ray Imager,an initial characterisation of the devices has been carried out to give a baseline performance level.Three CCDs have been characterised,the two flight devices and the flight spa re.This has been carried out at the Open University in a bespo ke cleanroom measure ment facility.The results show that there is a cluster of bright pixels in the flight spa re which increases in size with tempe rature.However at the nominal ope rating tempe rature(-120℃) it is within the procure ment specifications.Overall,the devices meet the specifications when ope rating at -120℃ in 6 × 6 binned frame transfer science mode.The se rial charge transfer inefficiency degrades with temperature in full frame mode.However any charge losses are recovered when binning/frame transfer is implemented.

Simulation of the SMILE Soft X-ray Imager response to a southward interplanetary magnetic field turning

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)Soft X-ray Imager(SXI)will shine a spotlight on magnetopause dynamics during magnetic reconnection.We simulate an event with a southward interplanetary magnetic field turning and produce SXI count maps with a 5-minute integration time.By making assumptions about the magnetopause shape,we find the magnetopause standoff distance from the count maps and compare it with the one obtained directly from the magnetohydrodynamic(MHD)simulation.The root mean square deviations between the reconstructed and MHD standoff distances do not exceed 0.2 RE(Earth radius)and the maximal difference equals 0.24 RE during the 25-minute interval around the southward turning.

Two-Dimensional Perovskite Single Crystals for High-Performance X-ray Imaging and Exploring MeV X-ray Detection

Scintillation semiconductors play increasingly important medical diagnosis and industrial inspection roles.Recently,two-dimensional(2D)perovskites have been shown to be promising materials for medical X-ray imaging,but they are mostly used in low-energy(≤130 keV)regions.Direct detection of MeV X-rays,which ensure thorough penetration of the thick shell walls of containers,trucks,and aircraft,is also highly desired in practical industrial applications.Unfortunately,scintillation semiconductors for high-energy X-ray detection are currently scarce.Here,This paper reports a 2D(C_(4)H_(9)NH_(3))_(2)PbBr_(4)single crystal with outstanding sensitivity and stability toward X-ray radiation that provides an ultra-wide detectable X-ray range of between 8.20 nGy_(air)s^(-1)(50 keV)and 15.24 mGy_(air)s^(-1)(9 MeV).The(C_(4)H_(9)NH_(3))_(2)PbBr_(4)single-crystal detector with a vertical structure is used for high-performance X-ray imaging,delivering a good spatial resolution of 4.3 Ip mm^(-1)in a plane-scan imaging system.Low ionic migration in the 2D perovskite enables the vertical device to be operated with hundreds of keV to MeV X-ray radiation at high bias voltages,leading to a sensitivity of 46.90μC Gy_(air)-1 cm^(-2)(-1.16 Vμm^(-1))with 9 MeV X-ray radiation,demonstrating that 2D perovskites have enormous potential for high-energy industrial applications.

An attention-based cascade R-CNN model for sternum fracture detection in X-ray images

Fracture is one of the most common and unexpected traumas.If not treated in time,it may cause serious consequences such as joint stiffness,traumatic arthritis,and nerve injury.Using computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection speed.However,there are still some problems in sternum fracture detection,such as the low detection rate of small and occult fractures.In this work,the authors have constructed a dataset with 1227 labelled X-ray images for sternum fracture detection.The authors designed a fully automatic fracture detection model based on a deep convolution neural network(CNN).The authors used cascade R-CNN,attention mechanism,and atrous convolution to optimise the detection of small fractures in a large X-ray image with big local variations.The authors compared the detection results of YOLOv5 model,cascade R-CNN and other state-of-the-art models.The authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71,which is much better than using the YOLOv5 model(mAP=0.44)and cascade R-CNN(mAP=0.55).

Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

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.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks

The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.

Insights into the hydrogen evolution reaction in vanadium redox flow batteries:A synchrotron radiation based X-ray imaging study

The parasitic hydrogen evolution reaction(HER)in the negative half-cell of vanadium redox flow batteries(VRFBs)causes severe efficiency losses.Thus,a deeper understanding of this process and the accompanying bubble formation is crucial.This benchmarking study locally analyzes the bubble distribution in thick,porous electrodes for the first time using deep learning-based image segmentation of synchrotron X-ray micro-tomograms.Each large three-dimensional data set was processed precisely in less than one minute while minimizing human errors and pointing out areas of increased HER activity in VRFBs.The study systematically varies the electrode potential and material,concluding that more negative electrode potentials of-200 m V vs.reversible hydrogen electrode(RHE)and lower cause more substantial bubble formation,resulting in bubble fractions of around 15%–20%in carbon felt electrodes.Contrarily,the bubble fractions stay only around 2%in an electrode combining carbon felt and carbon paper.The detected areas with high HER activity,such as the border subregion with more than 30%bubble fraction in carbon felt electrodes,the cutting edges,and preferential spots in the electrode bulk,are potential-independent and suggest that larger electrodes with a higher bulk-to-border ratio might reduce HER-related performance losses.The described combination of electrochemical measurements,local X-ray microtomography,AI-based segmentation,and 3D morphometric analysis is a powerful and novel approach for local bubble analysis in three-dimensional porous electrodes,providing an essential toolkit for a broad community working on bubble-generating electrochemical systems.

3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model

Structure reconstruction of 3 D anatomy from biplanar X-ray images is a challenging topic. Traditionally, the elastic-model-based method was used to reconstruct 3 D shapes by deforming the control points on the elastic mesh. However, the reconstructed shape is not smooth because the limited control points are only distributed on the edge of the elastic mesh.Alternatively, statistical-model-based methods, which include shape-model-based and intensity-model-based methods, are introduced due to their smooth reconstruction. However, both suffer from limitations. With the shape-model-based method, only the boundary profile is considered, leading to the loss of valid intensity information. For the intensity-based-method, the computation speed is slow because it needs to calculate the intensity distribution in each iteration. To address these issues, we propose a new reconstruction method using X-ray images and a specimen’s CT data. Specifically, the CT data provides both the shape mesh and the intensity model of the vertebra. Intensity model is used to generate the deformation field from X-ray images, while the shape model is used to generate the patient specific model by applying the calculated deformation field.Experiments on the public synthetic dataset and clinical dataset show that the average reconstruction errors are 1.1 mm and1.2 mm, separately. The average reconstruction time is 3 minutes.

Dental Age Estimation Based on X-ray Images

Chronological age estimation using panoramic dental X-ray images is an essential task in forensic sciences.Various statistical approaches have proposed by considering the teeth and mandible.However,building automated dental age estimation based on machine learning techniques needs more research efforts.In this paper,an automated dental age estimation is proposed using transfer learning.In the proposed approach,features are extracted using two deep neural networks namely,AlexNet and ResNet.Several classifiers are proposed to perform the classification task including decision tree,k-nearest neighbor,linear discriminant,and support vector machine.The proposed approach is evaluated using a number of suitable performance metrics using a dataset that contains 1429 dental X-ray images.The obtained results show that the proposed approach has a promising performance.

Anisotropy of Trabecular Bone from Ultra-Distal Radius Digital X-Ray Imaging: Effects on Bone Mineral Density and Age

Background: When applied to trabecular bone X-ray images, the anisotropic properties of trabeculae located at ultra-distal radius were investigated by using the trabecular bone scores (TBS) calculated along directions parallel and perpendicular to the forearm. Methodology: Data from more than two hundred subjects were studied retrospectively. A DXA (GE Lunar Prodigy) scan of the forearm was performed on each subject to measure the bone mineral density (BMD) value at the location of ultra-distal radius, and an X-ray digital image of the same forearm was taken on the same day. The values of trabecular bone score along the direction perpendicular to the forearm, TBSx, and along the direction parallel to the forearm, TBSy, were calculated respectively. The statistics of TBSx and TBSy were calculated, and the anisotropy of the trabecular bone, which was defined as the ratio of TBSy to TBSx and changed with subjects’ BMD and age, was reported and analyzed. Results: The results show that the correlation coefficient between TBSx and TBSy was 0.72 (p BMD and age was reported. The results showed that decreased trabecular bone anisotropy was associated with deceased BMD and increased age in the subject group. Conclusions: This study shows that decreased trabecular bone anisotropy was associated with decreased BMD and increased age.

Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread.This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment.X-ray images are one of the most classifiable images that are used widely in diagnosing patients’data depending on radiographs due to their structures and tissues that could be classified.Convolutional Neural Networks(CNN)is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy.Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results.In this paper,we used SqueezNet with a modified output layer to classify X-ray images into three groups:COVID-19,normal,and pneumonia.In this study,we propose a deep learning method with enhance the features of X-ray images collected from Kaggle,Figshare to distinguish between COVID-19,Normal,and Pneumonia infection.In this regard,several techniques were used on the selected image samples which are Unsharp filter,Histogram equal,and Complement image to produce another view of the dataset.The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type(COVID-19,Normal and Pneumonia).In the first scenario,the model has been tested without any enhancement on the datasets.It achieved an accuracy of 91%.But,in the second scenario,the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%.The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images.A comparison of the outcomes demonstrated the effectiveness of ourDLmethod for classifying COVID-19 based on enhanced X-ray images.

An Optimized CNN Model Architecture for Detecting Coronavirus (COVID-19) with X-Ray Images

This paper demonstrates empirical research on using convolutional neural networks(CNN)of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction.Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge.In this study,CNN architectures such as VGG-16,VGG-19,RestNet50,RestNet18 are compared,and an optimized model for feature extraction in X-ray images from various domains invol-ving several classes is proposed.An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for classifying COVID-19(Negative or Positive).Then,2,134 X-rays of normal patients and COVID-19 patients generated by an existing open-source online dataset were labeled to train the optimized models.Among those,the optimized model architecture classifier technique achieves higher accuracy(0.97)than four other models,specifically VGG-16,VGG-19,RestNet18,and RestNet50(0.96,0.72,0.91,and 0.93,respectively).Therefore,this study will enable radiol-ogists to more efficiently and effectively classify a patient’s coronavirus disease.

Prediction of Covid-19 Based on Chest X-Ray Images Using Deep Learning with CNN

The COVID-19 pandemic has caused trouble in people’s daily lives andruined several economies around the world, killing millions of people thus far. Itis essential to screen the affected patients in a timely and cost-effective manner inorder to fight this disease. This paper presents the prediction of COVID-19 withChest X-Ray images, and the implementation of an image processing systemoperated using deep learning and neural networks. In this paper, a Deep Learning,Machine Learning, and Convolutional Neural Network-based approach for predicting Covid-19 positive and normal patients using Chest X-Ray pictures is proposed. In this study, machine learning tools such as TensorFlow were used forbuilding and training neural nets. Scikit-learn was used for machine learning fromend to end. Various deep learning features are used, such as Conv2D, Dense Net,Dropout, Maxpooling2D for creating the model. The proposed approach had aclassification accuracy of 96.43 percent and a validation accuracy of 98.33 percentafter training and testing the X-Ray pictures. Finally, a web application has beendeveloped for general users, which will detect chest x-ray images either as covidor normal. A GUI application for the Covid prediction framework was run. Achest X-ray image can be browsed and fed into the program by medical personnelor the general public.

A Novel Method for Automated Lung Region Segmentation in Chest X-Ray Images

Detecting and segmenting the lung regions in chest X-ray images is an important part in artificial intelligence-based computer-aided diagnosis/detection (AI-CAD) systems for chest radiography. However, if the chest X-ray images themselves are used as training data for the AI-CAD system, the system might learn the irrelevant image-based information resulting in the decrease of system’s performance. In this study, we propose a lung region segmentation method that can automatically remove the shoulder and scapula regions, mediastinum, and diaphragm regions in advance from various chest X-ray images to be used as learning data. The proposed method consists of three main steps. First, employ the simple linear iterative clustering algorithm, the lazy snapping technique and local entropy filter to generate an entropy map. Second, apply morphological operations to the entropy map to obtain a lung mask. Third, perform automated segmentation of the lung field using the obtained mask. A total of 30 images were used for the experiments. In order to verify the effectiveness of the proposed method, two other texture maps, namely, the maps created from the standard deviation filtering and the range filtering, were used for comparison. As a result, the proposed method using the entropy map was able to appropriately remove the unnecessary regions. In addition, this method was able to remove the markers present in the image, but the other two methods could not. The experimental results have revealed that our proposed method is a highly generalizable and useful algorithm. We believe that this method might act an important role to enhance the performance of AI-CAD systems for chest X-ray images.

COVID-19 Detection from Chest X-Ray Images Using Convolutional Neural Network Approach

COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can range from mild to severe, and timely diagnosis is crucial for effective treatment. Chest X-Ray imaging is one diagnostic tool used for COVID-19, and a Convolutional Neural Network (CNN) is a popular technique for image classification. In this study, we proposed a CNN-based approach for detecting COVID-19 in chest X-Ray images. The model was trained on a dataset containing both COVID-19 positive and negative cases and evaluated on a separate test dataset to measure its accuracy. Our results indicated that the CNN approach could accurately detect COVID-19 in chest X-Ray images, with an overall accuracy of 97%. This approach could potentially serve as an early diagnostic tool to reduce the spread of the virus.