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Within the scope of this investigation, we carried out experiments to investigate the potential of the Vision Transformer (ViT) in the field of medical image analysis. The diagnosis of osteoporosis through inspection of X-ray radio-images is a substantial classification problem that we were able to address with the assistance of Vision Transformer models. In order to provide a basis for comparison, we conducted a parallel analysis in which we sought to solve the same problem by employing traditional convolutional neural networks (CNNs), which are well-known and commonly used techniques for the solution of image categorization issues. The findings of our research led us to conclude that ViT is capable of achieving superior outcomes compared to CNN. Furthermore, provided that methods have access to a sufficient quantity of training data, the probability increases that both methods arrive at more appropriate solutions to critical issues. [ABSTRACT FROM AUTHOR]

The emerging pseudohalide metal-free perovskite (pseudohalide–MFPs) X-ray detector caters to the demands of timely mobile diagnosis owing to its lightweight, flexibility, and cost-effectiveness. However, the performance of these devices is severely limited by poor X-ray absorption, ultra-wide band gap, relative instability, and their unknown biotoxicity. Herein, we construct heavy atom covalent bonds (C–Br/Cl) on the A-site organic cation to reinforce component coordination to modulate X-ray absorption and band gap in pseudohalide–MFPs and further enhance its stability. Molecular dynamics simulations demonstrate that the introduction of halogen atoms can strengthen hydrogen bonding interactions, thereby improving the coordination between different components. The resultant (MDABCOBr)–NH4(BF4)3 (MDABCO = N-methyl-N'-diazabicyclo[2.2.2]octonium) single crystal significantly increases X-ray absorption cross-section and crystalline density (from 1.728 to 1.950 g cm−3), and synergistically realizes the band nature modulation (from 7.4 to 5.5 eV) and enhanced ionic migration inhibition (628 meV) with optimized stability. As such, our X-ray detectors realized a sensitivity of 2377 μC Gyair−1 cm−2, an ultralow detection limit of 50.1 nGyair s−1, and impressive operation stability. Moreover, cytotoxicity assay confirmed the compatibility of pseudohalide metal-free perovskite. Finally, within this framework, we successfully fabricate the (MDABCOBr)–NH4(BF4)3-based flexible device to create an ideal in vitro wearable X-ray detection. [ABSTRACT FROM AUTHOR]

We report the photon (PL), electron (CL) and X-ray (XEL) induced luminescence characteristics of high aspect ratio ultra-long (~ 50 µm) ZnO nanorods (NRs) and discuss the potential for fast X-ray detection based on the consistent and efficient visible emission (~ 580 nm) from ZnO NRs. Nanostructured ZnO scintillators were rearranged to form a vertically well-aligned NR design in order to help light absorption and coupling resulting in luminescent and fast scintillation properties. The design of the nanorod array combines the key advantages of a low-cost growth technique together with environmentally friendly and widely available materials. A low temperature hydrothermal method was adopted to grow ZnO NRs in one cycle growth and their structural, optical and X-ray scintillation properties were investigated. The relatively short (~ 10 µm) ZnO NRs emitting in the near-band-edge region were found to be almost insensitive to X-rays. On the other hand, the higher XEL response of long ZnO NRs, which is a key parameter for evaluation of materials to be used as scintillators for high quality X-ray detection and imaging, along with a decay time response in the order of ns confirmed promising scintillation properties for fast and high-resolution X-ray detector applications. [ABSTRACT FROM AUTHOR]

Objective: Although there is growing evidence that ultrasonography is superior to X-ray for rib fractures' detection, X-ray is still indicated as the most appropriate method. This has partially been attributed to a lack of studies using an appropriate reference modality. We aimed to compare the diagnostic accuracy of ultrasonography and X-ray in the detection of rib fractures, considering CT as the reference standard. Materials and methods: Within a 2.5-year period, all consecutive patients with clinically suspected rib fracture(s) following blunt chest trauma and available posteroanterior/anteroposterior X-ray and thoracic CT were prospectively studied and planned to undergo thoracic ultrasonography, by a single operator. All imaging examinations were evaluated for cortical rib fracture(s), and their location was recorded. The cartilaginous rib portions were not assessed. CTs and X-rays were evaluated retrospectively. Concomitant thoracic/extra-thoracic injuries were assessed on CT. Comparisons were performed with the Mann–Whitney U test and Fisher's exact test. Results: Fifty-nine patients (32 males, 27 females; mean age, 53.1 ± 16.6 years) were included. CT, ultrasonography, and X-ray (40 posteroanterior/19 anteroposterior views) diagnosed 136/122/42 rib fractures in 56/54/27 patients, respectively. Ultrasonography and X-ray had sensitivity of 100%/40% and specificity of 89.7%/30.9% for rib fractures' detection. Ultrasound accuracy was 94.9% compared to 35.4% for X-rays (P <.001) in detecting individual rib fractures. Most fractures involved the 4th–9th ribs. Upper rib fractures were most commonly overlooked on ultrasonography. Thoracic cage/spine fractures and haemothorax represented the most common concomitant injuries. Conclusion: Ultrasonography appeared to be superior to X-ray for the detection of rib fractures with regard to a reference CT. [ABSTRACT FROM AUTHOR]

Human bone is an essential structure that allows the body to move. It is a common observation in contemporary society that bone fractures occur frequently. The doctors use X-rays, Computed Tomography scans, and Magnetic Resonance Imaging to determine the location of the broken bone. The previous method of evaluating broken bones in person was inefficient, often leading to errors. However, introducing new, advanced evaluation techniques has obliterated these issues. Consequently, it is essential to develop an automated system for identifying fractured bones. This research uses a new deep-learning model named "You Only Look Once (version 8)" to distinguish between healthy and broken bones from multi-modal images. We utilized a customized dataset named "Human Bone Fractures Multi-modal Image Dataset", which includes 641 images representing ten different classes of bone fractures. The small data set leads to an over-fitting of the model. To increase the amount of data, we utilized a data augmentation technique. Three experiments were conducted to assess the effectiveness of the model. The findings of the experiments show that the proposed study effectively identifies and classifies different types of fractures in this area. Our system attained 95% precision, 93% recall, and 92% of mean average precision. The outcomes demonstrated that the method achieves cutting-edge performance. [ABSTRACT FROM AUTHOR]

The density of shock-compressed liquid krypton ρ ≈ 9 g/cm3 and the temperature T ≈ 55 000 K on the main Hugoniot adiabat in the pressure range of Р ≈ 140–255 GPa have been measured with hemispherical shock wave generators. Using spherical devices, gaseous krypton has been compressed to the density ρ ≈ 20 g/cm3 by the pressure Р ≈ 2700 GPa at the temperature T ≈ 120 000 K. The experiment has been carried out at the X-ray diffraction complex RGK-B-L (Russian Federal Nuclear Center All-Russian Scientific Research Institute of Experimental Physics) consisting of BIM 234.3000 betatrons with a boundary energy of 60 MeV used in the multipulse bremsstrahlung generation regime with a multichannel optoelectronic detection system for X-ray images. The designs of the experimental devices have been described, and the thermodynamic parameters of the krypton plasma reached in these devices have been estimated. The obtained data have been analyzed and compared to previously reported data. The compression of krypton to the density ρ ≈ 20 g/cm3 by the pressure Р ≈ 2700 GPa is currently a record achievement. [ABSTRACT FROM AUTHOR]

Experiments on the dynamic isentropic compression of solid CO2 samples by the megabar pressure induced by the superstrong magnetic field of an explosive magnetic generator have been performed with the X-ray diffraction detection of the state of the compressed samples. The generator operation is based on the fast compression of the initial magnetic flux in the cavity of the generator by a conducting cylindrical liner accelerated by the products of the explosion of a cylindrical explosive charge. Two points at pressures of 349 and 459 GPa on the compressibility diagram of CO2 have been determined in the experiments, where the degree of compression ρ/ρ0 of CO2 has reached currently highest values of 3.90 and 4.02, respectively. Comparison has shown that theoretically calculated equations of states for crystal phases of CO2 almost completely reproduce the experimental results, which confirms a high accuracy of theoretical predictions and the identity of the experimental and theoretical equations of states of CO2 modifications stable at high pressures. [ABSTRACT FROM AUTHOR]

Objectives: Algorithms for fracture detection are spreading in clinical practice, but the use of X-ray-only ground truth can induce bias in their evaluation. This study assessed radiologists' performances to detect wrist and hand fractures on radiographs, using a commercially-available algorithm, compared to a computerized tomography (CT) ground truth. Methods: Post-traumatic hand and wrist CT and concomitant X-ray examinations were retrospectively gathered. Radiographs were labeled based on CT findings. The dataset was composed of 296 consecutive cases: 118 normal (39.9%), 178 pathological (60.1%) with a total of 267 fractures visible in CT. Twenty-three radiologists with various levels of experience reviewed all radiographs without AI, then using it, blinded towards CT results. Results: Using AI improved radiologists' sensitivity (Se, 0.658 to 0.703, p < 0.0001) and negative predictive value (NPV, 0.585 to 0.618, p < 0.0001), without affecting their specificity (Sp, 0.885 vs 0.891, p = 0.91) or positive predictive value (PPV, 0.887 vs 0.899, p = 0.08). On the radiographic dataset, based on the CT ground truth, stand-alone AI performances were 0.771 (Se), 0.898 (Sp), 0.684 (NPV), 0.915 (PPV), and 0.764 (AUROC) which were lower than previously reported, suggesting a potential underestimation of the number of missed fractures in the AI literature. Conclusions: AI enabled radiologists to improve their sensitivity and negative predictive value for wrist and hand fracture detection on radiographs, without affecting their specificity or positive predictive value, compared to a CT-based ground truth. Using CT as gold standard for X-ray labels is innovative, leading to algorithm performance poorer than reported elsewhere, but probably closer to clinical reality. Clinical relevance statement: Using an AI algorithm significantly improved radiologists' sensitivity and negative predictive value in detecting wrist and hand fractures on radiographs, with ground truth labels based on CT findings. Key Points: • Using CT as a ground truth for labeling X-rays is new in AI literature, and led to algorithm performance significantly poorer than reported elsewhere (AUROC: 0.764), but probably closer to clinical reality. • AI enabled radiologists to significantly improve their sensitivity (+ 4.5%) and negative predictive value (+ 3.3%) for the detection of wrist and hand fractures on X-rays. • There was no significant change in terms of specificity or positive predictive value. [ABSTRACT FROM AUTHOR]

Semiconductive metal–organic frameworks (MOFs) have attracted great interest for the electronic applications. However, dark currents of present hybrid organic–inorganic materials are 1000–10,000 times higher than those of commercial inorganic detectors, leading to poor charge transportation. Here, we demonstrate a ZIF-8 (Zn(mim)2, mim = 2-methylimidazolate) wafer with ultra-low dark current of 1.27 pA·mm−2 under high electric fields of 322 V·mm−1. The isostatic pressing preparation process provides ZIF-8 wafers with good transmittance. Besides, the presence of redox-active metals and small spatial separation between components promotes the charge hopping. The ZIF-8-based semiconductor detector shows promising X-ray detection sensitivity of 70.82 µC·Gy−1·cm−2 with low doses exposures, contributing to superior X-ray imaging capability with a relatively high spatial resolution of 1.2 Ip·mm−1. Simultaneously, good peak discrimination with the energy resolution of ∼ 43.78% is disclosed when the detector is illuminated by uncollimated 241Am@5.48 MeV α-particles. These results provide a broad prospect of MOFs for future radiation detection applications. [ABSTRACT FROM AUTHOR]

Challenges associated with stretchable optoelectronic devices, such as pixel size, power consumption and stability, severely brock their realization in high-resolution digital imaging. Herein, we develop a universal detachable interface technique that allows uniform, damage-free and reproducible integration of micropatterned stretchable electrodes for pixel-dense intrinsically stretchable organic transistor arrays. Benefiting from the ideal heterocontact and short channel length (2 μm) in our transistors, switching current ratio exceeding 106, device density of 41,000 transistors/cm2, operational voltage down to 5 V and excellent stability are simultaneously achieved. The resultant stretchable transistor-based image sensors exhibit ultrasensitive X-ray detection and high-resolution imaging capability. A megapixel image is demonstrated, which is unprecedented for stretchable direct-conversion X-ray detectors. These results forge a bright future for the stretchable photonic integration toward next-generation visualization equipment. Pixel size, power consumption, and stability of stretchable optoelectronic devices limit their application in digital imaging. Bian et al. developed a universal detachable interface technique for damage-free micropattern, reproducible transfer and ideal heterocontact of intrinsically stretchable electrodes. [ABSTRACT FROM AUTHOR]

Objectives: Artificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools. Methods: Findings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports. Results: Chest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001). Conclusion: The AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future. Critical relevance statement: With the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest. Key points: • A pipeline was developed for automated transfer of AI results into structured reports. • Pipeline chest X-ray reporting is faster than free-text or conventional structured reports. • Report quality was also rated higher for reports created with the pipeline. • The pipeline offers efficient, standardized AI integration into the clinical workflow. [ABSTRACT FROM AUTHOR]

Given that defect detection in weld X-ray images is a critical aspect of pressure vessel manufacturing and inspection, accurate differentiation of the type, distribution, number, and area of defects in the images serves as the foundation for judging weld quality, and the segmentation method of defects in digital X-ray images is the core technology for differentiating defects. Based on the publicly available weld seam dataset GDX-ray, this paper proposes a complete technique for fault segmentation in X-ray pictures of pressure vessel welds. The key works are as follows: (1) To address the problem of a lack of defect samples and imbalanced distribution inside GDX-ray, a DA-DCGAN based on a two-channel attention mechanism is devised to increase sample data. (2) A convolutional block attention mechanism is incorporated into the coding layer to boost the accuracy of small-scale defect identification. The proposed MAU-Net defect semantic segmentation network uses multi-scale even convolution to enhance large-scale features. The proposed method can mask electrostatic interference and non-defect-class parts in the actual weld X-ray images, achieve an average segmentation accuracy of 84.75% for the GDX-ray dataset, segment and accurately rate the valid defects with a correct rating rate of 95%, and thus realize practical value in engineering. [ABSTRACT FROM AUTHOR]

Metal-halide perovskite thin monocrystals featuring efficient carrier collection and transport capabilities are well suited for radiation detectors, yet their growth in a generic, well-controlled manner remains challenging. Here, we reveal that mass transfer is one major limiting factor during solution growth of perovskite thin monocrystals. A general approach is developed to overcome synthetic limitation by using a high solute flux system, in which mass diffusion coefficient is improved from 1.7×10–10 to 5.4×10–10 m2 s–1 by suppressing monomer aggregation. The generality of this approach is validated by the synthesis of 29 types of perovskite thin monocrystals at 40–90 °C with the growth velocity up to 27.2 μm min–1. The as-grown perovskite monocrystals deliver a high X-ray sensitivity of 1.74×105 µC Gy−1 cm−2 without applied bias. The findings regarding limited mass transfer and high-flux crystallization are crucial towards advancing the preparation and application of perovskite thin monocrystals. Liu et al. report a universal solution growth method for perovskite thin monocrystals by improving the mass transfer in the high solute flux system. The approach is applied to 29 types of perovskites with growth velocity up to 27.2 µm min-1 and enables efficient self-driven X-ray detectors. [ABSTRACT FROM AUTHOR]

Metal halide perovskites are becoming increasingly attractive for X-ray detection applications due to their distinctive optoelectronic properties. However, the practical use of lead-based organic–inorganic hybrid perovskites have been significantly impeded by their toxicity and instability. In this study, we have achieved successful growth lead-free bismuth-based all-inorganic perovskite Cs3Bi2Br9 films using a chemical vapor deposition (CVD) method for the first time. The CVD process has the advantage of circumventing the solubility limit of raw materials in conventional solution process, enabling thick film deposition for enhanced X-ray attenuation. Through meticulous adjustments to the molar ratio of the precursor materials, we have produced high-quality films characterized by a smooth surface and compact grains, ideal for minimizing leakage current. Consequently, the optimized devices exhibit an exceptionally low dark current density of 0.47 nA cm−2, meeting industrial standards for integration with thin-film transistor backplanes. Furthermore, these devices demonstrate an impressive sensitivity of 593 μC Gyair−1 cm−2, a low detection limit of 187.7 nGyair s−1, as well as excellent storage and operational stabilities, which hold great promise as an eco-friendly candidate for high-performance X-ray detection and imaging applications. [ABSTRACT FROM AUTHOR]

X-ray detection is widely used in various applications. However, to meet the demand for high image quality and high accuracy diagnosis, the raw data increases and imposes challenges for conventional X-ray detection hardware regarding data transmission and power consumption. To tackle these issues, we present a scheme of in-X-ray-detector computing based on CsPbBr3 single-crystal detector with convenient polarity reconfigurability, good linear dynamic range, and robust stability. The detector features a stable trap-free device structure and achieves a high linear dynamic range of 106 dB. As a result, the detector could achieve edge extraction imaging with a data compression ratio of ~50%, and could also be programmed and trained to perform pattern recognition tasks with a high accuracy of 100%. Our research shows that in-X-ray-detector computing can be used in flexible and complex scenarios, making it a promising platform for intelligent X-ray imaging. In-sensor computing requires detectors with polarity reconfigurability and linear responsivity. Pang et al. report a CsPbBr3 perovskite single crystal X-ray detector for edge extraction imaging with a data compression ratio of 46.4% and classification task with an accuracy of 100%. [ABSTRACT FROM AUTHOR]

The current challenge of wearable/implantable personal dosimeters for medical diagnosis and radiotherapy applications is lack of suitable detector materials possessing both excellent detection performance and biocompatibility. Here, we report a solution-grown biocompatible organic single crystalline semiconductor (OSCS), 4-Hydroxyphenylacetic acid (4HPA), achieving real-time spectral detection of charged particles with single-particle sensitivity. Along in-plane direction, two-dimensional anisotropic 4HPA exhibits a large electron drift velocity of 5 × 105 cm s−1 at "radiation-mode" while maintaining a high resistivity of (1.28 ± 0.003) × 1012 Ω·cm at "dark-mode" due to influence of dense π-π overlaps and high-energy L1 level. Therefore, 4HPA detectors exhibit the record spectra detection of charged particles among their organic counterparts, with energy resolution of 36%, (μt)e of (4.91 ± 0.07) × 10−5 cm2 V−1, and detection time down to 3 ms. These detectors also show high X-ray detection sensitivity of 16,612 μC Gyabs−1 cm−3, detection of limit of 20 nGyair s−1, and long-term stability after 690 Gyair irradiation. New detector materials are crucial for radiation beam monitoring in dosimeters and X-ray imaging. The authors report a solution-grown biocompatible organic single crystalline semiconductor for real-time spectral detection of charged particles with single-particle sensitivity, X-ray detection and imaging. [ABSTRACT FROM AUTHOR]

Mn-doped UO2 is under consideration for use as an accident tolerant nuclear fuel. We detail the synthesis of Mn-doped UO2 prepared via a wet co-precipitation method, which was refined to improve the yield of incorporated Mn. To verify the Mn-doped UO2 defect chemistry, X-ray absorption spectroscopy at the Mn K-edge was performed, in addition to X-ray diffraction, Raman spectroscopy and high-energy resolved fluorescence detection X-ray absorption near edge spectroscopy at the U M4-edge. It was established that Mn2+ directly substitutes for U4+ in the UO2 lattice, accompanied by oxygen vacancy (Ov) charge compensation. In contrast to other divalent-element doped UO2 materials, compelling evidence for U5+ in a charge compensating role was not found. This work furthers understanding of the structure and crystal chemistry of Mn-doped UO2, which could show potential advantages as a novel efficient advanced nuclear fuel. [ABSTRACT FROM AUTHOR]

Mn-doped UO2 is under consideration for use as an accident tolerant nuclear fuel. We detail the synthesis of Mn-doped UO2 prepared via a wet co-precipitation method, which was refined to improve the yield of incorporated Mn. To verify the Mn-doped UO2 defect chemistry, X-ray absorption spectroscopy at the Mn K-edge was performed, in addition to X-ray diffraction, Raman spectroscopy and high-energy resolved fluorescence detection X-ray absorption near edge spectroscopy at the U M4-edge. It was established that Mn2+ directly substitutes for U4+ in the UO2 lattice, accompanied by oxygen vacancy (Ov) charge compensation. In contrast to other divalent-element doped UO2 materials, compelling evidence for U5+ in a charge compensating role was not found. This work furthers understanding of the structure and crystal chemistry of Mn-doped UO2, which could show potential advantages as a novel efficient advanced nuclear fuel. [ABSTRACT FROM AUTHOR]

More than 200 million X-ray images are taken annually in the United States of America using several medical imaging procedures like conventional X-ray radiography, computed tomography (CT), fluoroscopy, and mammography. About 12% of mammograms result in false positives due to poor image resolution between dense breast tissues and cancerous tissues. False positive results lead to additional follow-up screening that cost billion s of dollars each year. This work aims to improve image resolution utilizing a novel direct conversion perovskite–graphene imaging detector that will enhance the X-ray detection sensitivity, which will improve the overall image quality compared to existing a-Se sensors installed in tomosynthesis and mammography units. A graphene field effect transistor perovskite sensor prototype made with metal halide perovskites was built and tested at Oregon State University. The sensor that was exposed to a micro-CT X-ray beam with 35 kVp, registered a sensitivity of 870 uC/cm2/R. The Planar design of the perovskite–graphene sensor was exposed to a 35 kVp and 50 kVp energy beams and registered a 500 uC/cm2/R and 350 uC/cm2/R sensitivity, respectively. The sensor sensitivity was found to be 3–4 orders of magnitude greater than current a-Se sensors used in mammography systems. Improvements in sensitivity should lead to better image quality, and thus reduce the percentage of false positive results, which will decrease the need for secondary screenings. [ABSTRACT FROM AUTHOR]

As a main electronic material, X-ray circuits are widely used in various electronic devices, and their quality has an important impact on the overall quality of electronic products. In the process of mass production of circuit boards, due to the large number of layers, tight lines and some harmful external factors, circuit board quality may be problematic. Detecting circuit board defects are important for improving the reliability of electronic products. This paper introduces deep learning and artificial intelligence technology to conduct research on the automatic detection of X-ray circuit board defects. The study used a defect detection system to study X-ray circuit boards as a detection object and obtained the structure, lighting system and composition of the detection system. The working principle of the detection system is explained, and the image is preprocessed. Testing the processing performance of the PCB defect detection system, when the number of pixels is 6526, 7028, 7530 and 8032, the time consumption ratios between the proposed detection system and image processing on a traditional PC are 35.17%, 35.4%, 35% and 35.28%, respectively. The experimental results make a certain contribution to the future artificial intelligence X-ray PCB defect automatic diagnosis algorithm. [ABSTRACT FROM AUTHOR]

This study investigates the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-ray images. Although current classifiers achieve high performance using chest X-ray images alone, consultations with practicing radiologists indicate that clinical data is highly informative and essential for interpreting medical images and making proper diagnoses. In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data). Since these data modalities are in different dimensional spaces, we propose a spatial arrangement strategy, spatialization, to facilitate the multimodal learning process in a Mask R-CNN model. We performed an extensive experimental evaluation using MIMIC-Eye, a dataset comprising different modalities: MIMIC-CXR (chest X-ray images), MIMIC IV-ED (patients' clinical data), and REFLACX (annotations of disease locations in chest X-rays). Results show that incorporating patients' clinical data in a DL model together with the proposed fusion methods improves the disease localization in chest X-rays by 12% in terms of Average Precision compared to a standard Mask R-CNN using chest X-rays alone. Further ablation studies also emphasize the importance of multimodal DL architectures and the incorporation of patients' clinical data in disease localization. In the interest of fostering scientific reproducibility, the architecture proposed within this investigation has been made publicly accessible(https://github.com/ChihchengHsieh/multimodal-abnormalities-detection). [ABSTRACT FROM AUTHOR]

The problem of detecting dangerous or prohibited objects in luggage is a very important step during the implementation of Security setup at Airports, Banks, Government buildings, etc. At present, the most common techniques for detecting such dangerous objects are by using intelligent data analysis algorithms such as deep learning techniques on X-ray imaging or employing a human workforce for inferring the presence of these threat objects in the obtained X-ray images. One of the major challenges while using deep-learning methods to detect such objects is the lack of high-quality threat image data containing the "dangerous" objects (objects of interest) versus the non-threat image data in practical scenarios. So, to tackle this data scarcity problem, anomaly detection techniques using normal data samples have shown great promise. Also, among the available Deep Learning Strategies for anomaly detection for computer vision applications, generative adversarial networks have achieved state-of-the-art results. Considering these insights, we adopted a newly proposed architecture known as Skip-GANomaly and devised a modified version of it by using a UNet++ style generator which performed better than Skip-GANomaly, getting an AUC of 94.94% on Compass-XP, a public X-ray dataset. Finally, for targeting better latent space exploration, we combine these two architectures into an Ensemble, which gives another boost to the performance, getting an AUC of 96.8% on the same Compass-XP, a public X-ray dataset. To further validate the effectiveness of ensemble-based architecture, its performance was tested on patch-based training data on a subset of randomly chosen images of another huge public X-ray dataset named as SIXray, and obtained an AUC of 75.3% on this reduced dataset. To demonstrate the prowess of the discriminator and to bring some explainability to the working of our ensemble, we have used Uniform Manifold Approximation and Projection to plot the latent-space vectors for the dangerous and non-dangerous objects of the test-set; this analysis indicates that the Ensemble learns better features for separating the anomalous class from non-anomalous with respect to the individual architectures. Thus, our proposed architecture provides state-of-the-art results for threat object detection. Most importantly, our models are able to detect threat objects without ever being trained on images containing threat objects. [ABSTRACT FROM AUTHOR]

Our study focuses on analysing the coronal, transition and chromospheric activity of four rapidly rotating stars located within 50 pc in the solar neighborhood. We have used the multi-wavelength capabilities of AstroSat to investigate the outer atmospheres of AB Dor, BO Mic, DG CVn and GJ 3331. These stars, classified as M and K type active stars, are known for their short rotation periods, leading to increased surface magnetic activity. Our soft X-ray observations provide the coronal properties, such as emission measures, temperatures and elemental coronal abundances. We reported the detection of X-ray flares from AB Dor, BO Mic and DG CVn, while UV light curves reveal flares in both BO Mic and DG CVn. [ABSTRACT FROM AUTHOR]

X-ray diffraction (XRD) is widely used in cellulose structural characterization. The commonly used "powder" XRD assumes the sample is macroscopically isotropic. For cellulose fibrous samples, however, due to the high aspect ratio of the components, the structure is often anisotropic, and the texture affects the materials properties to a large extent. A simple setup of a point-focused X-ray beam and a two-dimensional detection of scattered X-ray is a practical tool to analyze the texture. We studied three types of cellulose nanofibril (CNF) films obtained by casting. 2,2,6,6-tetramethylpiper- idine-1-oxyl radical (TEMPO) oxidized one shows a high degree of (1–10) uniplanar orientation, whereas holocellulose CNF and enzyme-pretreated CNF showed planar orientation. In the planar orientation, the c-axis is preferentially oriented in the plane parallel to the film while within each fibril other crystallographic axis would be randomly distributed around the c-axis. Also, a clear peak can be detected at low angle corresponding to a d-spacing of 3–4 nm indicating a strong correlation perpendicular to the film at this length scale. This distance was the lowest for TEMPO-CNF and corroborates with the model of uniplanar orientation of rectangular cross-section. The numerically simulated azimuthal intensity distribution of hk0 reflections in the two types of texture agreed well with the experimental intensity distribution. [ABSTRACT FROM AUTHOR]

Detecting detonators is a challenging task because they can be easily mis-classified as being a harmless organic mass, especially in high baggage throughput scenarios. Of particular interest is the focus on automated security X-ray analysis for detonators detection. The complex security scenarios require increasingly advanced combinations of computer-assisted vision. We propose an extensive set of experiments to evaluate the ability of Convolutional Neural Network (CNN) models to detect detonators, when the quality of the input images has been altered through manipulation. We leverage recent advances in the field of wavelet transforms and established CNN architectures—as both of these can be used for object detection. Various methods of image manipulation are used and further, the performance of detection is evaluated. Both raw X-ray images and manipulated images with the Contrast Limited Adaptive Histogram Equalization (CLAHE), wavelet transform-based methods and the mixed CLAHE RGB-wavelet method were analyzed. The results showed that a significant number of operations, such as: edges enhancements, altered color information or different frequency components provided by wavelet transforms, can be used to differentiate between almost similar features. It was found that the wavelet-based CNN achieved the higher detection performance. Overall, this performance illustrates the potential for a combined use of the manipulation methods and deep CNNs for airport security applications. [ABSTRACT FROM AUTHOR]

Edge detection is an essential computer vision phenomenon that is useful for contour detection and in turn it is useful for acquiring vital information. There are a variety of images, including medical, satellite, industrial and general-purpose images. An X-ray is a form of medical imaging that is created by transmitting electronic radiation into a patient's body to get an image of the human body's internal structures. This allows orthopedic surgeons and radiologists to make more accurate diagnoses of illness. Due to its versatility, soft computing approaches are regarded as effective edge detection tools. This study provides a novel fuzzy logic-based technique for edge detection in which the quality of edges is governed by a sharpening-guided filter. The proposed model holds four different patterns for extracting the edges from an image namely 2 × 2 mask, 3 × 2 mask, 2 × 3 mask and 3 × 3 mask. These patterns of masks target the edge pixels of an image along with the other pixels. The proposed model provides an enhanced approach for detecting edges in X-ray pictures of humans. Various statistical measurements are used to evaluate the proposed model to prove its significance. By adjusting the smoothing parameters appropriately, it has been discovered that the detected edges are significantly enhanced. The proposed model is implemented and evaluated under four different metrics namely Correlation Coefficient, Figure of Merit, Structure Similarity Image Metrics and Euclidian Distance. The proposed model results are compared five different state-of-the-art algorithms to prove the significance, and the result shows that the proposed model identifies the edge in a well-structured manner. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019, initially named 2019-nCOV (COVID-19) has been declared a global pandemic by the World Health Organization in March 2020. Because of the growing number of COVID patients, the world's health infrastructure has collapsed, and computer-aided diagnosis has become a necessity. Most of the models proposed for the COVID-19 detection in chest X-rays do image-level analysis. These models do not identify the infected region in the images for an accurate and precise diagnosis. The lesion segmentation will help the medical experts to identify the infected region in the lungs. Therefore, in this paper, a UNet-based encoder–decoder architecture is proposed for the COVID-19 lesion segmentation in chest X-rays. To improve performance, the proposed model employs an attention mechanism and a convolution-based atrous spatial pyramid pooling module. The proposed model obtained 0.8325 and 0.7132 values of the dice similarity coefficient and jaccard index, respectively, and outperformed the state-of-the-art UNet model. An ablation study has been performed to highlight the contribution of the attention mechanism and small dilation rates in the atrous spatial pyramid pooling module. [ABSTRACT FROM AUTHOR]

Dental caries is one of the most chronic diseases involving the majority of the population during their lifetime. Caries lesions are typically diagnosed by general dentists relying only on their visual inspection using dental x-rays. In many cases, dental caries is hard to identify in x-rays and can be misinterpreted as shadows due to the low image quality. In this research study, we propose an automatic diagnosis system to detect dental caries in Panoramic images, which benefits from various deep pretrained models through transfer learning to extract relevant features and uses a capsule network to draw prediction results. Using a dataset of 470 Panoramic images, our model achieved an accuracy of 86.05% on the test set. The obtained score demonstrates acceptable detection performance and an increase in caries detection speed, as long as the challenges of using Panoramic x-rays are taken into account. Among carious samples, our model acquired recall scores of 69.44% and 90.52% for mild and severe ones, confirming the fact that severe caries spots are more straightforward to detect and efficient mild caries detection needs a larger dataset. Considering the novelty of current study as using Panoramic images, following work is a step towards developing a fully automated system to assist domain experts. [ABSTRACT FROM AUTHOR]

Coronavirus, a virus that spread worldwide rapidly and was eventually declared a pandemic. The rapid spread made it essential to detect Coronavirus infected people to control the further spread. Recent studies show that radiological images such as X-Rays and CT scans provide essential information in detecting infection using deep learning models. This paper proposes a shallow architecture based on Capsule Networks with convolutional layers to detect COVID-19 infected persons. The proposed method combines the ability of the capsule network to understand spatial information with convolutional layers for efficient feature extraction. Due to the model's shallow architecture, it has 23M parameters to train and requires fewer training samples. The proposed system is fast and robust and correctly classifies the X-Ray images into three classes, i.e. COVID-19, No Findings, and Viral Pneumonia. Experimental results on the X-Ray dataset show that our model performs well despite having fewer samples for the training and achieved an average accuracy of 96.47% for multi-class and 97.69% for binary classification on 5-fold cross-validation. The proposed model would be useful to researchers and medical professionals for assistance and prognosis for COVID-19 infected patients. [ABSTRACT FROM AUTHOR]

Inorganic perovskite wafers with good stability and adjustable sizes are promising in X-ray detection but the high synthetic temperature is a hindrance. Herein, dimethyl sulfoxide (DMSO) is used to prepare the CsPbBr3 micro-bricks powder at room temperature. The CsPbBr3 powder has a cubic shape with few crystal defects, small charge trap density, and high crystallinity. A trace amount of DMSO attaches to the surface of the CsPbBr3 micro-bricks via Pb-O bonding, forming the CsPbBr3-DMSO adduct. During hot isostatic processing, the released DMSO vapor merges the CsPbBr3 micro-bricks, producing a compact and dense CsPbBr3 wafer with minimized grain boundaries and excellent charge transport properties. The CsPbBr3 wafer shows a large mobility-lifetime (μτ) product of 5.16 × 10−4 cm2·V−1, high sensitivity of 14,430 μC·Gyair−1·cm−2, low detection limit of 564 nGyair·s−1, as well as robust stability in X-ray detection. The results reveal a novel strategy with immense practical potential pertaining to high-contrast X-ray detection. [ABSTRACT FROM AUTHOR]

COVID-19 has expanded overall across the globe after its initial cases were discovered in December 2019 in Wuhan—China. Because the virus has impacted people's health worldwide, its fast identification is essential for preventing disease spread and reducing mortality rates. The reverse transcription polymerase chain reaction (RT-PCR) is the primary leading method for detecting COVID-19 disease; it has high costs and long turnaround times. Hence, quick and easy-to-use innovative diagnostic instruments are required. According to a new study, COVID-19 is linked to discoveries in chest X-ray pictures. The suggested approach includes a stage of pre-processing with lung segmentation, removing the surroundings that do not provide information pertinent to the task and may result in biased results. The InceptionV3 and U-Net deep learning models used in this work process the X-ray photo and classifies them as COVID-19 negative or positive. The CNN model that uses a transfer learning approach was trained. Finally, the findings are analyzed and interpreted through different examples. The obtained COVID-19 detection accuracy is around 99% for the best models. [ABSTRACT FROM AUTHOR]

Digital radiographic imaging technique plays an essential role in the non-destructive testing of gas turbine blades. Due to the scattered X-rays in the detection of gas turbine blade, the quality of digital radiographic image is limited, and the effective information may be missed, which makes it difficult to characterize defects. Based on the simple scattering model and dark channel priori theory, an enhancement method for digital radiographic images of gas turbine blades is presented in this paper. Using the digital radiographic testing system, the proposed method is verified in the detection of uncooled 3rd and 4th stage gas turbine blades which is composed of nickel-based high temperature alloys. The experimental results show that the proposed method can dramatically enhance the visibility of digital radiographic images, including the improvement of contrast-noise ratio and sensitivity. In the meantime, the tiny defects that are not visible in the initial image can be identified and the detection can be more accurately characterized in the enhanced digital radiographic images. The proposed method can assist in de-scattering applications and improve the visibility of digital radiographic images without precise information about the X-ray condition. [ABSTRACT FROM AUTHOR]

Highlights: The classification of 2D perovskite is summarized, and the preparation methods of 2D perovskite according to the requirements of X-ray detection materials are introduced. We analyzed the advantages and insufficiency of different devices and introduced improvement measures, including ion migration, charge transfer performance, stability, and 2D/3D heterojunctions. Finally, we introduced the potential preponderances of 2D perovskite in the scintillation detection field; meanwhile, the main challenges facing the practical application of 2D perovskite X-ray detectors are analyzed. Metal halide perovskites have recently emerged as promising candidates for the next generation of X-ray detectors due to their excellent optoelectronic properties. Especially, two-dimensional (2D) perovskites afford many distinct properties, including remarkable structural diversity, high generation energy, and balanced large exciton binding energy. With the advantages of 2D materials and perovskites, it successfully reduces the decomposition and phase transition of perovskite and effectively suppresses ion migration. Meanwhile, the existence of a high hydrophobic spacer can block water molecules, thus making 2D perovskite obtain excellent stability. All of these advantages have attracted much attention in the field of X-ray detection. This review introduces the classification of 2D halide perovskites, summarizes the synthesis technology and performance characteristics of 2D perovskite X-ray direct detector, and briefly discusses the application of 2D perovskite in scintillators. Finally, this review also emphasizes the key challenges faced by 2D perovskite X-ray detectors in practical application and presents our views on its future development. [ABSTRACT FROM AUTHOR]

Detecting low dose rates of X-rays is critical for making safer radiology instruments, but is limited by the absorber materials available. Here, we develop bismuth oxyiodide (BiOI) single crystals into effective X-ray detectors. BiOI features complex lattice dynamics, owing to the ionic character of the lattice and weak van der Waals interactions between layers. Through use of ultrafast spectroscopy, first-principles computations and detailed optical and structural characterisation, we show that photoexcited charge-carriers in BiOI couple to intralayer breathing phonon modes, forming large polarons, thus enabling longer drift lengths for the photoexcited carriers than would be expected if self-trapping occurred. This, combined with the low and stable dark currents and high linear X-ray attenuation coefficients, leads to strong detector performance. High sensitivities reaching 1.1 × 103 μC Gyair−1 cm−2 are achieved, and the lowest dose rate directly measured by the detectors was 22 nGyair s−1. The photophysical principles discussed herein offer new design avenues for novel materials with heavy elements and low-dimensional electronic structures for (opto)electronic applications. The complex coupling between charge-carriers and phonons in bismuth oxyiodide (BiOI) are uncovered, showing how carrier localisation is avoided and long transport lengths achieved. As a result, BiOI is revealed to be highly effective for X-ray detection. [ABSTRACT FROM AUTHOR]

Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results. [ABSTRACT FROM AUTHOR]

Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series of datasets where bias management has not been thorough from the point of view of patient information, capture conditions, class imbalance, and careless mixtures of multiple datasets. This paper analyses 19 datasets of COVID-19 chest X-ray images, identifying potential biases. Moreover, computational experiments were conducted using one of the most popular datasets in this domain, which obtains a 96.19% of classification accuracy on the complete dataset. Nevertheless, when evaluated with the ethical tool Aequitas, it fails on all the metrics. Ethical tools enhanced with some distribution and image quality considerations are the keys to developing or choosing a dataset with fewer bias issues. We aim to provide broad research on dataset problems, tools, and suggestions for future dataset developments and COVID-19 applications using chest X-ray images. [ABSTRACT FROM AUTHOR]

We investigated, using DFT calculations, the electronic, optical, polaron properties and estimate the upper light yield of Tl 2 ZrCl 6 material. The DFT calculations were performed with GGA+mBJ and GGA+mBJ+SOC approximations to obtain precise electronic, optical and polaron properties. The calculated results showed that Tl 2 ZrCl 6 has localized valence and narrow discrete conduction bands with many sharp peaks and low dispersion which reflects the heavier effective mass of electron at CBM, 4.63 m e , in comparison with that of hole at VBM, 1.05 m e . Indirect band gap energy of 4.30 eV was observed and which is close to the experimental value, 4.22 eV. Additionally, in the emission range, this material exhibited a high transmittance rate of around 90% which is an advantage of the scintillation properties. Following DFT calculations, polaron properties and also the temperature-dependence of polaron mobility and relaxation time were calculated by solving the Feynman polaron model, variationally, with the free-energies minimization. The obtained results showed that Tl 2 ZrCl 6 has a large electron (hole)-phonon coupling, α e = 8.55 ( α h = 4.1) , leading to large polaron mass, small relaxation time, high scattering rate and low electron (hole) mobility of 0.13 cm 2 V - 1 s - 1 (4.17 cm 2 V - 1 s - 1 ) at room-temperature. Such low mobility has a positive role only in the initial ionization track by increasing the recombination yield. Moreover, using the polaron and simple phenomenological models, the upper light yield was estimated to be 87,757 p h / M e V and 75,187 p h / M e V . Such findings could have important implications for understanding the scintillation behavior of this material to improve its performance as a potential scintillator for γ- and X-rays detection. [ABSTRACT FROM AUTHOR]

Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%. [ABSTRACT FROM AUTHOR]

At present, bone fracture is a common clinical disease, while the missed diagnosis or misdiagnosis of fracture is harmful to the recovery of patients. Fracture diagnosis often needs the X-ray image as an assistive tool and many fracture detection CAD systems on X-ray images have been explored. However, the majority of existing works mainly focus on detecting fractures in a specific human body part. It's desirable and feasible to propose a more practical system that can detect various anatomical region fractures ideally due to their similar general fracture characteristics. In this paper, a universal fracture detection CAD system has been developed by us on X-ray images based on the deep learning method. Firstly, we design an image preprocessing method to improve the poor quality of these X-ray images and employ several data augmentation strategies to enlarge the used dataset. Secondly, based on our modified Ada-ResNeSt backbone network and the AC-BiFPN detection method, we propose our automatic fracture detection system. Finally, we establish a private universal fracture detection dataset MURA-D based on the public dataset MURA. As demonstrated by our comprehensive experiments, compared with other popular detectors, our method achieved a higher detection AP of 68.4% with an acceptable inference speed of 122 ms per image on the MURA-D test set, achieving promising results among the state-of-the-art detectors. [ABSTRACT FROM AUTHOR]

Coronavirus disease-2019 (COVID-19) is a serious infectious disease that is spreading rapidly all over the world. Scientists are looking for alternative diagnostic methods to detect and control the disease early. Artificial intelligence applications are promising in the COVID-19 epidemic. This paper proposes a hybrid approach for diagnosing COVID-19 on chest X-ray images and differentiation from other viral pneumonia. The model we propose consists of three steps. In the first step, classification was made using the MobilenetV2, Efficientnetb0, and Darknet53 deep models. In the second step, the feature maps of the images in the Chest X-ray data set were extracted separately for each architecture using the MobilenetV2, Efficientnetb0, and Darknet53 architectures. NCA method was preferred to reduce the size of these feature maps obtained. The feature maps obtained after dimension reduction were classified in the classic machine learning classifiers. In the third step, the feature maps obtained from each architecture were combined. After dimension reduction was applied to these combined features by applying the NCA method, this feature map is classified in the classifiers. The model we proposed was tested on two different data sets. The accuracy values obtained in these data sets are 99.05 and 97.1%, respectively. The obtained accuracy values show that the model is successful. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs) are a class of fascinating supramolecular crystalline materials that have been widely developed for catalysis, gas storage, illumination, drug delivery/cytoprotection, and so on. Recently, MOFs have been found to have potential applications in X-ray detection due to their high sensitivity, fast response time, high absorption coefficient, and radiation stability. In this review, we present an overview on the fundamental mechanism of using MOFs for X-ray scintillation. We further discuss the recent developments in X-ray detection based on indirect X-ray scintillation and direct X-ray conversion. Finally, we provide a summary and a perspective on the future of this promising research field. [ABSTRACT FROM AUTHOR]

Prohibited item detection from X-ray images determines whether any prohibited items are present in baggage, and great progress has recently been made in this field with the development of deep learning. Nevertheless, the appearance of an occluded item interacts with the cover, which is different from occlusions encountered in conventional object detection. We design three mechanisms to handle this challenge on the assumption that the occluded part is still partially observed. First, we propose a scale interaction module in which the features in neighboring scales interact one or more times to enhance the model's perception ability. Then, we design a cross-image weakly supervised semantic analysis model utilizing the coattention mechanism to perceive similar and different targets, breaking through the information bottleneck of the isolated detection of a single image. Finally, we introduce a multitask learning module to simultaneously optimize the model at the global level and pixel level. We evaluate our approach on the publicly available security inspection X-ray (SIXray) dataset, the occluded prohibited items X-ray (OPIXray) dataset, and the HIXray dataset, and the results show that our approach is competitive with other X-ray baggage inspection approaches. [ABSTRACT FROM AUTHOR]

The computer-aided diagnosis (CAD) for chest X-rays was developed more than 50 years ago. However, there are still unmet needs for its versatile use in our medical fields. We planned this study to develop a multipotent CAD model suitable for general use including in primary care areas. We planned this study to solve the problem by using computed tomography (CT) scan with its one-to-one matched chest X-ray dataset. The data was extracted and preprocessed by pulmonology experts by using the bounding boxes to locate lesions of interest. For detecting multiple lesions, multi-object detection by faster R-CNN and by RetinaNet was adopted and compared. A total of twelve diagnostic labels were defined as the followings: pleural effusion, atelectasis, pulmonary nodule, cardiomegaly, consolidation, emphysema, pneumothorax, chemo-port, bronchial wall thickening, reticular opacity, pleural thickening, and bronchiectasis. The Faster R-CNN model showed higher overall sensitivity than RetinaNet, nevertheless the values of specificity were opposite. Some values such as cardiomegaly and chemo-port showed excellent sensitivity (100.0%, both). Others showed that the unique results such as bronchial wall thickening, reticular opacity, and pleural thickening can be described in the chest area. As far as we know, this is the first study to develop an object detection model for chest X-rays based on chest area defined by CT scans in one-to-one matched manner, preprocessed and conducted by a group of experts in pulmonology. Our model can be a potential tool for detecting the whole chest area with multiple diagnoses from a simple X-ray that is routinely taken in most clinics and hospitals on daily basis. [ABSTRACT FROM AUTHOR]

Wrist and finger fractures detection is always the weak point of associate study, because there are small targets in X-rays, such as hairline fractures. In this paper, a dataset, consisting of 4346 anteroposterior, lateral and oblique hand X-rays, is built from many orthopedic cases. Specifically, it contains a lot of hairline fractures. An automatic preprocessing based on generative adversative network (GAN) and a detection network, called WrisNet, are designed to improve the detection performance of wrist and finger fractures. In the preprocessing, an attention mechanism-based GAN is proposed for obtaining the approximation of manual windowing enhancement. A multiscale attention-module-based generator of the GAN is proposed to increase continuity between pixels. The discriminator and the generator can achieve 93% structural similarity (SSIM) as manual windowing enhancement without manual parameter adjustment. The designed WrisNet is composed of two components: a feature extraction module and a detection module. A group convolution and a lightweight but efficient triplet attention mechanism are elaborately embedded into the feature extraction module, resulting in richer representations of hairline fractures. To obtain more accurate locating information in this condition, the soft non-maximum suppression algorithm is employed as the post-processing method of the detection module. As shown in experimental results, the designed method can have obvious average precision (AP) improvement up to 7% or more than other mainstream frameworks. The automatic preprocessing and the detection net can greatly reduce the degree of artificial intervention, so it is easy to be implemented in real clinical environment. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease, which originated in Wuhan, developed into a severe public health problem worldwide. Immense stress in the society and health department was advanced due to the multiplying numbers of COVID carriers and deaths. This stress can be lowered by performing a high-speed diagnosis for the disease, which can be a crucial stride for opposing the deadly virus. A good large amount of time is consumed in the diagnosis. Some applications that use medical images like X-Rays or CT-Scans can pace up the time used in diagnosis. Hence, this paper aims to create a computer-aided-design system that will use the chest X-Ray as input and further classify it into one of the three classes, namely COVID-19, viral Pneumonia, and healthy. Since the COVID-19 positive chest X-Rays dataset was low, we have exploited four pre-trained deep neural networks (DNNs) to find the best for this system. The dataset consisted of 2905 images with 219 COVID-19 cases, 1341 healthy cases, and 1345 viral pneumonia cases. Out of these images, the models were evaluated on 30 images of each class for the testing, while the rest of them were used for training. It is observed that AlexNet attained an accuracy of 97.6% with an average precision, recall, and F1 score of 0.98, 0.97, and 0.98, respectively. [ABSTRACT FROM AUTHOR]

Metal halide perovskites (MHPs) have demonstrated excellent performances in detection of X-rays and gamma-rays. Most studies focus on improving the sensitivity of single-pixel MHP detectors. However, little work pays attention to the dark current, which is crucial for the back-end circuit integration. Herein, the requirement of dark current is quantitatively evaluated as low as 10−9 A/cm2 for X-ray imagers integrated on pixel circuits. Moreover, through the semiconductor device analysis and simulation, we reveal that the main current compositions of thick perovskite X-ray detectors are the thermionic-emission current (JT) and the generation-recombination current (Jg-r). The typical observed failures of p–n junctions in thick detectors are caused by the high generation-recombination current due to the band mismatch and interface defects. This work provides a deep insight into the design of high sensitivity and low dark current perovskite X-ray detectors. [ABSTRACT FROM AUTHOR]

Microalgal biomasses, consisting of micronized Spirulina Platensis and its low protein fraction, were investigated in this work as possible renewable biofillers in natural rubber compounds, with the aim of replacing the commonly used carbon black. Natural rubber, in some cases blended with 10% of epoxidized natural rubber to improve the matrix-filler affinity, was compounded with 25, 35, 50 and 75 phr of each biomass. Compounds with 25, 35 and 50 phr of carbon black N990 were also prepared as benchmarks. After compounding, vulcanization times were determined by dynamic mechanical analysis. Rubbers were vulcanized by compression moulding and characterized by means of morphological analysis (scanning electron microscopy), thermal analysis (thermogravimetric analysis, dynamic mechanical thermal analysis) and mechanical tests (tensile tests, strain induced crystallization detection by X-ray diffraction, pure shear fracture tests). Microalgal biomass turned out to be homogeneously dispersed in natural rubber matrix and the materials obtained required lower curing times compared to carbon black compounds. It was found that, up to 50 phr, Spirulina has the ability to increase rubber tensile strength and modulus, acting similarly to N990, while decreasing rubber thermal stability and fracture toughness. [ABSTRACT FROM AUTHOR]

This document is a retraction note for an article titled "Novel fuzzy logic expert system-based edge detection for X-ray images" published in the journal Soft Computing. The publisher has retracted the article due to concerns about compromised editorial handling and peer review processes, as well as inappropriate references and being out of scope for the journal. The authors of the article disagree with the retraction. The retraction note provides contact information for the authors and their affiliations. [Extracted from the article]

The coronavirus occurred in Wuhan (China) first and it was declared a global pandemic. To detect coronavirus X-ray images can be used. Convolutional neural networks (CNNs) are used commonly to detect illness from images. There can be lots of different alternative deep CNN models or architectures. To find the best architecture, hyper-parameter optimization can be used. In this study, the problem is modeled as a multi-objective optimization (MOO) problem. Objective functions are multi-class cross entropy, error ratio, and complexity of the CNN network. For the best solutions to the objective functions, multi-objective hyper-parameter optimization is made by NSGA-III, NSGA-II, R-NSGA-II, SMS-EMOA, MOEA/D, and proposed Swarm Genetic Algorithms (SGA). SGA is a swarm-based algorithm with a cross-over process. All six algorithms are run and give Pareto optimal solution sets. When the figures obtained from the algorithms are analyzed and algorithm hypervolume values are compared, SGA outperforms the NSGA-III, NSGA-II, R-NSGA-II, SMS-EMOA, and MOEA/D algorithms. It can be concluded that SGA is better than others for multi-objective hyper-parameter optimization algorithms for COVID-19 detection from X-ray images. Also, a sensitivity analysis has been made to understand the effect of the number of the parameters of CNN on model success. [ABSTRACT FROM AUTHOR]

Scintillators, which are widely used as radiation detectors, are phosphors that release absorbed ionizing radiation energy as ultraviolet or visible light. Inorganic glass scintillators have several advantages over inorganic crystal scintillators, such as ease of fabrication and low costs. However, unlike inorganic crystals, which can emit up to tens of thousands of photons/MeV, inorganic glasses exhibit less than several hundred photons/MeV in most cases. Here, we studied an inorganic glass scintillator that exhibits a light yield of 2700 photons/MeV, which exceeds those of previous inorganic glass scintillators with high light yields of approximately 2000 photons/MeV. The density of this material is 3.28 g/cm3, which is relatively high among glass scintillators. Moreover, a fast scintillation decay with a decay time constant of 30.0 ns was obtained and is attributed to the 5d–4f transition of Ce3+. Thus, this glass is suitable for gamma- and X-ray detection, thereby expanding the practical applicability of inorganic glass scintillators. [ABSTRACT FROM AUTHOR]