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The process of pneumonia detection has been the focus of researchers as it has proved itself to be one of the most dangerous and life-threatening disorders. In recent years, many machine learning and deep learning algorithms have been applied in an attempt to automate this process but none of them has been successful significantly to achieve the highest possible accuracy. In a similar attempt, we propose an enhanced approach of a deep learning model called restricted Boltzmann machine (RBM) which is named enhanced RBM (ERBM). One of the major drawbacks associated with the standard format of RBM is its random weight initialization which leads to improper feature learning of the model during the training phase, resulting in poor performance of the machine. This problem has been tried to eliminate in this work by finding the differences between the means of a specific feature vector and the means of all features given as inputs to the machine. By performing this process, the reconstruction of the actual features is increased which ultimately reduces the error generated during the training phase of the model. The developed model has been applied to three different datasets of pneumonia diseases and the results have been compared with other state of the art techniques using different performance evaluation parameters. The proposed model gave highest accuracy of 98.56% followed by standard RBM, SVM, KNN, and decision tree which gave accuracies of 97.53%, 92.62%, 91.64%, and 88.77%, respectively, for dataset named dataset 2. Similarly, for the dataset 1, the highest accuracy of 96.66 has been observed for the eRBM followed by srRBM, KNN, decision tree, and SVM which gave accuracies of 90.22%, 89.34%, 87.65%, and 86.55%, respectively. In the same way, the accuracies observed for the dataset 3 by eRBM, standard RBM, KNN, decision tree, and SVM are 92.45%, 90.98%, 87.54%, 85.49%, and 84.54%, respectively. Similar observations can also be seen for other performance parameters showing the efficiency of the proposed model. As revealed in the results obtained, a significant improvement has been observed in the working of the RBM by introducing a new method of weight initialization during the training phase. The results show that the improved model outperforms other models in terms of different performance evaluation parameters, namely, accuracy, sensitivity, specificity, F1-score, and ROC curve. [ABSTRACT FROM AUTHOR]

Pneumonia represents a life-endangering and deadly disease that results from a viral or bacterial infection in the human lungs. The earlier pneumonia's diagnosing is an essential aspect in the processes of successful treatment. Recently, the developed methods of deep learning that include several layers of processing to comprehend the stratified data representation have obtained the best results in various domains, especially in the identification and classification of human diseases. Therefore, for improving the systems' performance for detecting pneumonia disease, there is a requirement for implementing automatic models based on deep learning models that have the ability to diagnose the images of chest X-rays and to facilitate the detection process of pneumonia novices and experts. A convolutional neural network (CNN) model is developed in this paper for detecting pneumonia via utilizing the images of chest X-rays. The proposed framework encompasses two main stages: the stage of image preprocessing and the stage of extracting features and image classification. The proposed CNN model provides high results of precision, recall, F1-score, and accuracy by 98%, 98%, 97%, and 99.82%, respectively. Regarding the obtained results, the proposed CNN model-based pneumonia detection has achieved a better result of consistency and accuracy, and it has outperformed the other pretrained deep learning models such as residual networks (ResNet 50) and VGG16. Furthermore, it exceeds the recently existing models presented in the literature. Thus, the significant performance of the proposed CNN model-based pneumonia detection in all measures of performance can provide effective services of patient care and decrease the rates of mortality. [ABSTRACT FROM AUTHOR]

Scintillators with high light yield (LY) values are of interest for medical imaging applications, in harsh environments, nondestructive testing (NDT), etc. CeBr3 has a LY of 60000 photons per MeV, a value much higher than other efficient materials, such as Lu3Al5O12:Ce (25000 photons/MeV); thus, its X-ray detection properties would be of interest to be examined for medical imaging applications. The X-ray detection and absorption properties of a single crystal CeBr3 sample along with the compatibility of its produced light with various optoelectronic sensors were examined. In this study, the quantum detection (QDE) and the energy absorption efficiency (EAE) of CeBr3 were calculated. The findings were compared with data for 10 × 10 × 10 m m 3 Lu3Al5O12:Ce and CaF2:Eu single crystals. The measured optical spectrum produced by CeBr3 was well correlated with the spectral response of commercial optical sensors, yielding spectral matching higher than 93% for various photocathodes, e.g., GaAs (94%), E-S20 (95%), and bialkali and multialkali (95-97%), as well as with flat panel position-sensitive photomultipliers (95-99%). The energy absorption properties of CeBr3 were found higher than those of Lu3Al5O12:Ce and CaF2:Eu for X-ray tube voltages greater than 100 kVp. The quantum detection efficiency was 100% across the examined energy range. Even though CeBr3 is hygroscopic and has a mediocre 5.1 g/cm3 density, the QDE, EAE, and spectral correlation results are promising for medical imaging applications. [ABSTRACT FROM AUTHOR]

For personal safety and crime prevention, some research studies based on deep learning have achieved success in the object detection of X-ray security inspection. However, the research on dangerous liquid detection is still scarce, and most research studies are focused on the detection of some prohibited and common items. In this paper, a lightweight dangerous liquid detection method based on the Depthwise Separable convolution for X-ray security inspection is proposed. Firstly, a dataset of seven common dangerous liquids with multiple postures in two detection environments is established. Secondly, we propose a novel detection framework using the dual-energy X-ray data instead of pseudocolor images as the objects to be detected, which improves the detection accuracy and realizes the parallel operation of detection and imaging. Thirdly, in order to ensure the detection accuracy and reduce the computational consumption and the number of parameters, based on the Depthwise Separable convolution and the Squeeze-and-Excitation block, a lightweight object location network and a lightweight dangerous liquid classification network are designed as the backbone networks of our method to achieve the location and classification of the dangerous liquids, respectively. Finally, a semiautomatic labeling method is proposed to improve the efficiency of data labeling. Compared with the existing methods, the experimental results demonstrate that our method has better performance and wider applicability. [ABSTRACT FROM AUTHOR]

Since the outbreak of Coronavirus disease 2019 (COVID-19), it has been spreading rapidly worldwide and has not yet been effectively controlled. Many researchers are studying novel Coronavirus pneumonia from chest X-ray images. In order to improve the detection accuracy, two modules sensitive to feature information, dual-path multiscale feature fusion module and dense depthwise separable convolution module, are proposed. Based on these two modules, a lightweight convolutional neural network model, D2-CovidNet, is designed to assist experts in diagnosing COVID-19 by identifying chest X-ray images. D2-CovidNet is tested on two public data sets, and its classification accuracy, precision, sensitivity, specificity, and F1-score are 94.56%, 95.14%, 94.02%, 96.61%, and 95.30%, respectively. Specifically, the precision, sensitivity, and specificity of the network for COVID-19 are 98.97%, 94.12%, and 99.84%, respectively. D2-CovidNet has fewer computation number and parameter number. Compared with other methods, D2-CovidNet can help diagnose COVID-19 more quickly and accurately. [ABSTRACT FROM AUTHOR]

Objective. To improve the clinical detection rate of bone and joint fractures of the extremities and to explore the value and significance of the application of multislice spiral computed tomography (MSCT) postprocessing technology in diagnosis. Methods. 80 patients with bone and joint fractures of the extremities admitted to the hospital were selected as the research objects. The patients received X-ray digital radiography (DR) plain film examination and then MSCT examination. At the same time, multiplane reconstruction (MPR) and surface shadow display (SSD) and volume rendering three-dimensional imaging (VRT) technology and other postprocessing technologies compare the differences in the detection rate of limbs and joint fractures between the two inspection methods. Results. A total of 100 fractures were found in 80 patients. The detection rate of X-ray DR was 69%. After MSCT postprocessing technology, the detection rates of MPR, SSD, and VRT were 96%, 98%, and 99%, respectively. The accuracy of MSCT postprocessing technology in diagnosing extremity bone and joint fractures was significantly higher than that of DR, and the difference between groups was statistically significant. Conclusion. MSCT postprocessing technology for patients with extremity bone and joint fractures has a good effect. It is not only noninvasive but also has a high detection rate. It can significantly reduce the missed and misdiagnosed rate and provide detailed imaging data for the formulation of clinical treatment plans. [ABSTRACT FROM AUTHOR]

The unconfined compressive strength of cement-modified silty sand in Jilin Province was investigated in this study. For this purpose, various tests were conducted, including the screening test, compaction test, CBR test, X-ray fluorescence detection, and unconfined compressive strength test. Effects of compaction degree, soil quality, water quality, cement content, and curing age were considered. The results show that CBR value is positively correlated with compactness. Two kinds of different water qualities have little effect on unconfined compressive strength of cement-improved soil; with the increase in cement content, the unconfined compressive strength increases, and the power function equation established by the two is significantly correlated. The logarithmic relationship between cement-soil strength and curing age is approximately linear. Through regression analysis, the comprehensive characterization parameters of cement-soil strength, such as water-cement ratio, cement content, and curing age, are put forward. The unconfined compressive strength of cement-modified silty sand has a good power function relationship with the comprehensive characterization parameters, and the fitting degree between the strength prediction formula and the existing research and test data exceeds 90%, which verifies the effectiveness of the comprehensive characterization parameters. [ABSTRACT FROM AUTHOR]

In March 2020, the World Health Organization announced the COVID-19 pandemic, its dangers, and its rapid spread throughout the world. In March 2021, the second wave of the pandemic began with a new strain of COVID-19, which was more dangerous for some countries, including India, recording 400,000 new cases daily and more than 4,000 deaths per day. This pandemic has overloaded the medical sector, especially radiology. Deep-learning techniques have been used to reduce the burden on hospitals and assist physicians for accurate diagnoses. In our study, two models of deep learning, ResNet-50 and AlexNet, were introduced to diagnose X-ray datasets collected from many sources. Each network diagnosed a multiclass (four classes) and a two-class dataset. The images were processed to remove noise, and a data augmentation technique was applied to the minority classes to create a balance between the classes. The features extracted by convolutional neural network (CNN) models were combined with traditional Gray-level Cooccurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms in a 1-D vector of each image, which produced more representative features for each disease. Network parameters were tuned for optimum performance. The ResNet-50 network reached accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 95%, 94.5%, 98%, and 97.10%, respectively, with the multiclasses (COVID-19, viral pneumonia, lung opacity, and normal), while it reached accuracy, sensitivity, specificity, and AUC of 99%, 98%, 98%, and 97.51%, respectively, with the binary classes (COVID-19 and normal). [ABSTRACT FROM AUTHOR]

The world is experiencing an unprecedented crisis due to the coronavirus disease (COVID-19) outbreak that has affected nearly 216 countries and territories across the globe. Since the pandemic outbreak, there is a growing interest in computational model-based diagnostic technologies to support the screening and diagnosis of COVID-19 cases using medical imaging such as chest X-ray (CXR) scans. It is discovered in initial studies that patients infected with COVID-19 show abnormalities in their CXR images that represent specific radiological patterns. Still, detection of these patterns is challenging and time-consuming even for skilled radiologists. In this study, we propose a novel convolutional neural network- (CNN-) based deep learning fusion framework using the transfer learning concept where parameters (weights) from different models are combined into a single model to extract features from images which are then fed to a custom classifier for prediction. We use gradient-weighted class activation mapping to visualize the infected areas of CXR images. Furthermore, we provide feature representation through visualization to gain a deeper understanding of the class separability of the studied models with respect to COVID-19 detection. Cross-validation studies are used to assess the performance of the proposed models using open-access datasets containing healthy and both COVID-19 and other pneumonia infected CXR images. Evaluation results show that the best performing fusion model can attain a classification accuracy of 95.49% with a high level of sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts' image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. In the first scenario, the model has been tested using the 100 X-ray images of the original processed dataset which achieved an accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of COVID-19 X-ray images. The performance in this test scenario was as high as 99.5%. To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set. [ABSTRACT FROM AUTHOR]

We present an X-ray material classifier region-based convolutional neural network (XMC R-CNN) model for detecting the typical guns and the typical knives in X-ray baggage images. The XMC R-CNN model is used to solve the problem of contraband detection in overlapped X-ray baggage images by the X-ray material classifier algorithm and the organic stripping and inorganic stripping algorithm, and better detection rate and the miss rate are achieved. The detection rates of guns and knives are 96.5% and 95.8%, and the miss rates of guns and knives are 2.2% and 4.2%. The contraband detection technology based on the XMC R-CNN model is applied to X-ray baggage images of security inspection. According to user needs, the safe X-ray baggage images can be automatically filtered in some specific fields, which reduces the number of X-ray baggage images that security inspectors need to screen. The efficiency of security inspection is improved, and the labor intensity of security inspection is reduced. In addition, the security inspector can screen X-ray baggage images according to the boxes of automatic detection, which can improve the effect of security inspection. [ABSTRACT FROM AUTHOR]

Weld defects detection using X-ray images is an effective method of nondestructive testing. Conventionally, this work is based on qualified human experts, although it requires their personal intervention for the extraction and classification of heterogeneity. Many approaches have been done using machine learning (ML) and image processing tools to solve those tasks. Although the detection and classification have been enhanced with regard to the problems of low contrast and poor quality, their result is still unsatisfying. Unlike the previous research based on ML, this paper proposes a novel classification method based on deep learning network. In this work, an original approach based on the use of the pretrained network AlexNet architecture aims at the classification of the shortcomings of welds and the increase of the correct recognition in our dataset. Transfer learning is used as methodology with the pretrained AlexNet model. For deep learning applications, a large amount of X-ray images is required, but there are few datasets of pipeline welding defects. For this, we have enhanced our dataset focusing on two types of defects and augmented using data augmentation (random image transformations over data such as translation and reflection). Finally, a fine-tuning technique is applied to classify the welding images and is compared to the deep convolutional activation features (DCFA) and several pretrained DCNN models, namely, VGG-16, VGG-19, ResNet50, ResNet101, and GoogLeNet. The main objective of this work is to explore the capacity of AlexNet and different pretrained architecture with transfer learning for the classification of X-ray images. The accuracy achieved with our model is thoroughly presented. The experimental results obtained on the weld dataset with our proposed model are validated using GDXray database. The results obtained also in the validation test set are compared to the others offered by DCNN models, which show a best performance in less time. This can be seen as evidence of the strength of our proposed classification model. [ABSTRACT FROM AUTHOR]

The aim of the present study was to examine the image quality performance of a CMOS digital imaging optical sensor coupled to custom made gadolinium oxysulfide powder scintillators, doped with praseodymium, cerium, and fluorine (Gd2O2S:Pr,Ce,F). The screens, with coating thicknesses 35.7 and 71.2 mg/cm2, were prepared in our laboratory from Gd2O2S:Pr,Ce,F powder (Phosphor Technology, Ltd.) by sedimentation on silica substrates and were placed in direct contact with the optical sensor. Image quality was determined through single index (information capacity, IC) and spatial frequency dependent parameters, by assessing the Modulation Transfer Function (MTF) and the Normalized Noise Power Spectrum (NNPS). The MTF was measured using the slanted-edge method. The CMOS sensor/Gd2O2S:Pr,Ce,F screens combinations were irradiated under the RQA-5 (IEC 62220-1) beam quality. The detector response function was linear for the exposure range under investigation. Under the general radiography conditions, both Gd2O2S:Pr,Ce,F screen/CMOS combinations exhibited moderate imaging properties, in terms of IC, with previously published scintillators, such as CsI:Tl, Gd2O2S:Tb, and Gd2O2S:Eu. [ABSTRACT FROM AUTHOR]

Currently industrial X-CT system is designed according to characteristics of test objects, and test objects determine industrial X-CT system structure, X-ray detector/sensor property, scanning mode, and so forth. So there are no uniform standards for the geometry size of scintillation crystals of detector. Moreover, scintillation crystals are usually mixed with some highly toxic impurity elements, such as Tl and Cd. Thus, it is indispensable for establishing guidelines of engineering practice to simulate X-ray detection performances of different scintillation crystals. This paper focuses on how to achieve high efficient X-ray detection in industrial XCT system which usedMonte Carlo (MC) method to study X-ray energy straggling characteristics, full energy peak efficiency, and conversion efficiency of some scintillation crystals (e.g.,CsI(Tl),NaI(Tl), andCdWO4) afterX-ray interacted with these scintillation crystals. Our experimental results demonstrate that CsI(Tl) scintillation crystal has the advantages of conversion efficiency, spectral matching, manufacturing process, and full energy peak efficiency; it is an ideal choice for high efficient X-ray detection in industrial X-CT system. [ABSTRACT FROM AUTHOR]