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Journal articles on the topic 'Neural networks; X-ray crystallography'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Sullivan, Brendan, Rick Archibald, Jahaun Azadmanesh, et al. "BraggNet: integrating Bragg peaks using neural networks." Journal of Applied Crystallography 52, no. 4 (2019): 854–63. http://dx.doi.org/10.1107/s1600576719008665.

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Neutron crystallography offers enormous potential to complement structures from X-ray crystallography by clarifying the positions of low-Z elements, namely hydrogen. Macromolecular neutron crystallography, however, remains limited, in part owing to the challenge of integrating peak shapes from pulsed-source experiments. To advance existing software, this article demonstrates the use of machine learning to refine peak locations, predict peak shapes and yield more accurate integrated intensities when applied to whole data sets from a protein crystal. The artificial neural network, based on the U-Net architecture commonly used for image segmentation, is trained using about 100 000 simulated training peaks derived from strong peaks. After 100 training epochs (a round of training over the whole data set broken into smaller batches), training converges and achieves a Dice coefficient of around 65%, in contrast to just 15% for negative control data sets. Integrating whole peak sets using the neural network yields improved intensity statistics compared with other integration methods, including k-nearest neighbours. These results demonstrate, for the first time, that neural networks can learn peak shapes and be used to integrate Bragg peaks. It is expected that integration using neural networks can be further developed to increase the quality of neutron, electron and X-ray crystallography data.

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Ke, Tsung-Wei, Aaron S. Brewster, Stella X. Yu, Daniela Ushizima, Chao Yang, and Nicholas K. Sauter. "A convolutional neural network-based screening tool for X-ray serial crystallography." Journal of Synchrotron Radiation 25, no. 3 (2018): 655–70. http://dx.doi.org/10.1107/s1600577518004873.

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A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization.

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Ito, Sho, Go Ueno, and Masaki Yamamoto. "DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography." Journal of Synchrotron Radiation 26, no. 4 (2019): 1361–66. http://dx.doi.org/10.1107/s160057751900434x.

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High-throughput protein crystallography using a synchrotron light source is an important method used in drug discovery. Beamline components for automated experiments including automatic sample changers have been utilized to accelerate the measurement of a number of macromolecular crystals. However, unlike cryo-loop centering, crystal centering involving automated crystal detection is a difficult process to automate fully. Here, DeepCentering, a new automated crystal centering system, is presented. DeepCentering works using a convolutional neural network, which is a deep learning operation. This system achieves fully automated accurate crystal centering without using X-ray irradiation of crystals, and can be used for fully automated data collection in high-throughput macromolecular crystallography.

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Baek, Minkyung, Frank DiMaio, Ivan Anishchenko, et al. "Accurate prediction of protein structures and interactions using a three-track neural network." Science 373, no. 6557 (2021): 871–76. http://dx.doi.org/10.1126/science.abj8754.

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DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo–electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.

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Xuan, Wenjing, Ning Liu, Neng Huang, Yaohang Li, and Jianxin Wang. "CLPred: a sequence-based protein crystallization predictor using BLSTM neural network." Bioinformatics 36, Supplement_2 (2020): i709—i717. http://dx.doi.org/10.1093/bioinformatics/btaa791.

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Abstract Motivation Determining the structures of proteins is a critical step to understand their biological functions. Crystallography-based X-ray diffraction technique is the main method for experimental protein structure determination. However, the underlying crystallization process, which needs multiple time-consuming and costly experimental steps, has a high attrition rate. To overcome this issue, a series of in silico methods have been developed with the primary aim of selecting the protein sequences that are promising to be crystallized. However, the predictive performance of the current methods is modest. Results We propose a deep learning model, so-called CLPred, which uses a bidirectional recurrent neural network with long short-term memory (BLSTM) to capture the long-range interaction patterns between k-mers amino acids to predict protein crystallizability. Using sequence only information, CLPred outperforms the existing deep-learning predictors and a vast majority of sequence-based diffraction-quality crystals predictors on three independent test sets. The results highlight the effectiveness of BLSTM in capturing non-local, long-range inter-peptide interaction patterns to distinguish proteins that can result in diffraction-quality crystals from those that cannot. CLPred has been steadily improved over the previous window-based neural networks, which is able to predict crystallization propensity with high accuracy. CLPred can also be improved significantly if it incorporates additional features from pre-extracted evolutional, structural and physicochemical characteristics. The correctness of CLPred predictions is further validated by the case studies of Sox transcription factor family member proteins and Zika virus non-structural proteins. Availability and implementation https://github.com/xuanwenjing/CLPred.

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Elbasir, Abdurrahman, Balasubramanian Moovarkumudalvan, Khalid Kunji, Prasanna R. Kolatkar, Raghvendra Mall, and Halima Bensmail. "DeepCrystal: a deep learning framework for sequence-based protein crystallization prediction." Bioinformatics 35, no. 13 (2018): 2216–25. http://dx.doi.org/10.1093/bioinformatics/bty953.

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Abstract Motivation Protein structure determination has primarily been performed using X-ray crystallography. To overcome the expensive cost, high attrition rate and series of trial-and-error settings, many in-silico methods have been developed to predict crystallization propensities of proteins based on their sequences. However, the majority of these methods build their predictors by extracting features from protein sequences, which is computationally expensive and can explode the feature space. We propose DeepCrystal, a deep learning framework for sequence-based protein crystallization prediction. It uses deep learning to identify proteins which can produce diffraction-quality crystals without the need to manually engineer additional biochemical and structural features from sequence. Our model is based on convolutional neural networks, which can exploit frequently occurring k-mers and sets of k-mers from the protein sequences to distinguish proteins that will result in diffraction-quality crystals from those that will not. Results Our model surpasses previous sequence-based protein crystallization predictors in terms of recall, F-score, accuracy and Matthew’s correlation coefficient (MCC) on three independent test sets. DeepCrystal achieves an average improvement of 1.4, 12.1% in recall, when compared to its closest competitors, Crysalis II and Crysf, respectively. In addition, DeepCrystal attains an average improvement of 2.1, 6.0% for F-score, 1.9, 3.9% for accuracy and 3.8, 7.0% for MCC w.r.t. Crysalis II and Crysf on independent test sets. Availability and implementation The standalone source code and models are available at https://github.com/elbasir/DeepCrystal and a web-server is also available at https://deeplearning-protein.qcri.org. Supplementary information Supplementary data are available at Bioinformatics online.

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Uddin, Mostofa Rafid, Sazan Mahbub, M. Saifur Rahman, and Md Shamsuzzoha Bayzid. "SAINT: self-attention augmented inception-inside-inception network improves protein secondary structure prediction." Bioinformatics 36, no. 17 (2020): 4599–608. http://dx.doi.org/10.1093/bioinformatics/btaa531.

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Abstract Motivation Protein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g. X-ray crystallography and nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the SS of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of SS contains more useful information and is much more challenging than the Q3 prediction. Results We present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception network in order to effectively capture both the short- and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step toward the accurate and reliable prediction of SSs of proteins. Availability and implementation SAINT is freely available as an open-source project at https://github.com/SAINTProtein/SAINT.

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van den Bedem, Henry, Gira Bhabha, Kun Yang, Peter E. Wright, and James S. Fraser. "Automated identification of functional dynamic contact networks from X-ray crystallography." Nature Methods 10, no. 9 (2013): 896–902. http://dx.doi.org/10.1038/nmeth.2592.

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Inokuma, Yasuhide, and Makoto Fujita. "Visualization of Solution Chemistry by X-ray Crystallography Using Porous Coordination Networks." Bulletin of the Chemical Society of Japan 87, no. 11 (2014): 1161–76. http://dx.doi.org/10.1246/bcsj.20140217.

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10

Romo, T., K. Gopal, E. McKee, et al. "TEXTAL: AI-Based Structural Determination for X-ray Protein Crystallography." IEEE Intelligent Systems 20, no. 6 (2005): 59–63. http://dx.doi.org/10.1109/mis.2005.114.

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Larsen, J. T., W. L. Morgan, and W. H. Goldstein. "Artificial neural networks for plasma x‐ray spectroscopic analysis." Review of Scientific Instruments 63, no. 10 (1992): 4775–77. http://dx.doi.org/10.1063/1.1143558.

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12

Naskinova, I. "On Convolutional Neural Networks for Chest X-ray Classification." IOP Conference Series: Materials Science and Engineering 1031, no. 1 (2021): 012075. http://dx.doi.org/10.1088/1757-899x/1031/1/012075.

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Chen, Julian, Bryant Hanson, S. Fisher, et al. "Ultra-high resolution neutron and X-ray crystallography: structure of crambin." Acta Crystallographica Section A Foundations and Advances 70, a1 (2014): C1206. http://dx.doi.org/10.1107/s2053273314087932.

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Neutron diffraction data to 1.1 Å was collected on a crystal of the small protein crambin at the Protein Crystallography Station (PCS) at Los Alamos, the highest resolution neutron structure of a protein to date, and a technical benchmark for the instrument. 95 % of the hydrogen atoms in the protein structure were resolved. The data allowed for the refinement of anisotropic temperature factors for selected deuterium atoms within the protein. Hydrogen bonding networks ambiguous in room temperature, ultra-high resolution (0.84 Å) electron density maps are clarified in the nuclear density maps. The ultra-high resolution data also reveals unusual H/D exchange patterns and novel chemistry in the side chains and protein backbone. Complementary X-ray diffraction data was collected at 19-ID at the Advanced Photon Source, with extensive re-configuration of the beamline to allow for operation at higher energy settings.

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14

Boone, J. M. "X-ray spectral reconstruction from attenuation data using neural networks." Medical Physics 17, no. 4 (1990): 647–54. http://dx.doi.org/10.1118/1.596495.

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AlSumairi, Sarah Badr, and Mohamed Maher Ben Ismail. "X-ray image based pneumonia classification using convolutional neural networks." ACCENTS Transactions on Image Processing and Computer Vision 6, no. 20 (2020): 54–67. http://dx.doi.org/10.19101/tipcv.2020.618050.

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Pneumonia is an infectious disease of the lungs. About one third to one half of pneumonia cases are caused by bacteria. Early diagnosis is a critical factor for a successful treatment process. Typically, the disease can be diagnosed by a radiologist using chest X-ray images. In fact, chest X-rays are currently the best available method for diagnosing pneumonia. However, the recognition of pneumonia symptoms is a challenging task that relies on the availability of expert radiologists. Such “human” diagnosis can be inaccurate and subjective due to lack of clarity and erroneous decision. Moreover, the error can increase more if the physician is requested to analyze tens of X-rays within a short period of time. Therefore, Computer-Aided Diagnosis (CAD) systems were introduced to support and assist physicians and make their efforts more productive. In this paper, we investigate, design, implement and assess customized Convolutional Neural Networks to overcome the image-based Pneumonia classification problem. Namely, ResNet-50 and DenseNet-161 models were inherited to design customized deep network architecture and improve the overall pneumonia classification accuracy. Moreover, data augmentation was deployed and associated with standard datasets to assess the proposed models. Besides, standard performance measures were used to validate and evaluate the proposed system.

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Jain, Ashish. "Pneumonia Detection from Chest X-Rays using Neural Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 910–13. http://dx.doi.org/10.22214/ijraset.2021.36489.

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Pneumonia is one of the most serious diseases which cause the most deaths in the world. Viruses, bacteria, and fungi can cause pneumonia. The infection from spreading to the lungs in the human body. In order to diagnose this infection, a chest x-ray is carried out. The doctor uses X-ray image in order to diagnose or monitor the treatment of states in which inflammation of the lungs. X-rays are also used in the diagnosis of diseases such as emphysema, lung cancer, cancer of the line, and pipe, and tuberculosis (tb). However, a diagnosis of pneumonia requiring medical experts to comment on its presence felt in the chest x-ray. For decades, the auto- diagnosis (CAD) systems have been used for the respiratory disease based on chest X-ray images. Deep learning allows machines can quickly extract and classify objects from a photo. Ilham, with the great success of deep learning, we use a deep learning approach to detection of pneumonia into the work. Convolutional neural network that was developed for this study is the inflammation of the lungs. Supervised learning is ANCHORED to the use of features and functions. In general, the data of 5826 images with the help of one of the Kaggle.com. The CNN training and testing, that is, an open set of data. In the proposed method, the high success rate of accurate classification is achieved.

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Day, Charles R., James C. Austin, John B. Butcher, Peter W. Haycock, and Anthony T. Kearon. "Element-specific determination of X-ray transmission signatures using neural networks." NDT & E International 42, no. 5 (2009): 446–51. http://dx.doi.org/10.1016/j.ndteint.2009.02.005.

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Salehinejad, Hojjat, Errol Colak, Tim Dowdell, Joseph Barfett, and Shahrokh Valaee. "Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks." IEEE Transactions on Medical Imaging 38, no. 5 (2019): 1197–206. http://dx.doi.org/10.1109/tmi.2018.2881415.

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Li, Fei, Zhixing Gu, Liangquan Ge, et al. "Application of artificial neural networks to X‐ray fluorescence spectrum analysis." X-Ray Spectrometry 48, no. 2 (2018): 138–50. http://dx.doi.org/10.1002/xrs.2996.

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Arias-Garzón, Daniel, Jesús Alejandro Alzate-Grisales, Simon Orozco-Arias, et al. "COVID-19 detection in X-ray images using convolutional neural networks." Machine Learning with Applications 6 (December 2021): 100138. http://dx.doi.org/10.1016/j.mlwa.2021.100138.

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Czyzewski, Adam, Faustyna Krawiec, Dariusz Brzezinski, Przemyslaw Jerzy Porebski, and Wladek Minor. "Detecting anomalies in X-ray diffraction images using convolutional neural networks." Expert Systems with Applications 174 (July 2021): 114740. http://dx.doi.org/10.1016/j.eswa.2021.114740.

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Mahesh, Pillalamarry, Yakkala Gnana Prathyusha, Botlagunta Sahithi, and S. Nagendram. "Covid-19 Detection from Chest X-Ray using Convolution Neural Networks." Journal of Physics: Conference Series 1804, no. 1 (2021): 012197. http://dx.doi.org/10.1088/1742-6596/1804/1/012197.

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Al Nasr, Kamal, and Qasem Abu Al-Haija. "Forecasting the Growth of Structures from NMR and X-Ray Crystallography Experiments Released Per Year." Journal of Information & Knowledge Management 19, no. 01 (2020): 2040004. http://dx.doi.org/10.1142/s0219649220400043.

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In this paper, we present a forecasting scheme for the growth of molecular structures from NMR and X-ray Crystallography experimental techniques released every year by employing an autoregressive (AR) process. The proposed scheme maximises the forecasting accuracy by utilising the optimal AR process order. The optimal model order was derived as the model with the least prediction error. Therefore, the proposed scheme has been efficiently employed to model and predict the annual growth of structures-based NMR and X-ray Crystallography experimental data for the next decade 2019–2028 using the time series of the past 43 years of both experimental datasets. The experimental results showed that the optimal model order to estimate both datasets was [Formula: see text] which belongs to a forecasting accuracy of [Formula: see text], for both datasets. Indeed, such a high level of accuracy referred to the amount of linearity between the consecutive elements of the original times series. Hence, the forecasting results reveals of an exponential increasing behaviour in the future growth in the annual structures released from both NMR and X-ray Crystallography experiments.

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García-Raso, Angel, Angel Terrón, Adela López-Zafra та ін. "Crystal structures of N6-modified-amino acid related nucleobase analogs (II): hybrid adenine-β-alanine and adenine-GABA molecules". New Journal of Chemistry 43, № 24 (2019): 9680–88. http://dx.doi.org/10.1039/c9nj02279a.

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Puneet Gupta. "Pneumonia Detection Using Convolutional Neural Networks." January 2021 7, no. 01 (2021): 77–80. http://dx.doi.org/10.46501/ijmtst070117.

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Abstract— Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumoniae. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia, need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained CNN models on large-scale datasets are much useful in image classification tasks. In this work, we appraise the functionality of pre-trained CNN models utilized as feature-extractors followed by different classifiers for the classification of abnormal and normal chest X-Rays. We analytically determine the optimal CNN model for the purpose. Statistical results obtained demonstrates that pretrained CNN models employed along with supervised classifier algorithms can be very beneficial in analyzing chest X-ray images, specifically to detect Pneumonia. In this project Transfer learning and a CNN Model is used to detect whether the person has pneumonia or not using chest x-ray.

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Kim, Byungwhan, and Min-Geun Park. "Prediction of Surface Roughness Using X-Ray Photoelectron Spectroscopy and Neural Networks." Applied Spectroscopy 60, no. 10 (2006): 1192–97. http://dx.doi.org/10.1366/000370206778664554.

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Chen, Yen-Lin, and Lois Pollack. "Convolutional Neural Networks Bridge Molecular Models and Solution X-ray Scattering Experiments." Biophysical Journal 118, no. 3 (2020): 301a. http://dx.doi.org/10.1016/j.bpj.2019.11.1706.

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Liu, Shaobo, Frank Y. Shih, and Xin Zhong. "Classification of Chest X-Ray Images Using Novel Adaptive Morphological Neural Networks." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 10 (2021): 2157006. http://dx.doi.org/10.1142/s0218001421570068.

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The chest X-ray images are difficult to classify for the radiologists due to the noisy nature. The existing models based on convolutional neural networks contain a giant number of parameters, and thus require multi-advanced GPUs to deploy. In this paper, we are the first to develop the adaptive morphological neural networks to classify chest X-ray images, such as pneumonia and COVID-19. A novel structure, which can self-learn morphological dilation and erosion, is proposed to determine the most suitable depth of the adaptive layer. Experimental results on the chest X-ray and the COVID-19 datasets show that the proposed model can achieve the highest classification rate as compared against the existing models. Moreover, it can significantly reduce the computational parameters of the existing models by 97%. The advantage makes the developed model more attractive than others to deploy in the internet and other device platforms.

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Devi, L. Nirmala, and K. Venkata Subbareddy. "COVID-19 detection through convolutional neural networks and chest X-ray images." International Journal of Medical Engineering and Informatics 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijmei.2021.10041116.

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García-Raso, Angel, Angel Terrón, Bartomeu Balle, et al. "Crystal structures of N6-modified-amino acid nucleobase analogs(iii): adenine–valeric acid, adenine–hexanoic acid and adenine–gabapentine." New Journal of Chemistry 44, no. 28 (2020): 12236–46. http://dx.doi.org/10.1039/d0nj02538k.

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H-bonding networks, anion–π and π–π interactions in the crystal structures of N<sup>6</sup>-modified-amino acid adenine analogs are investigated by means of DFT calculations and X-ray crystallography analysis.

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Mishra, Risha, and Raghavaiah Pallepogu. "Supramolecular heterosynthon assemblies of ortho-phenylenediamine with substituted aromatic carboxylic acids." Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials 74, no. 1 (2018): 32–41. http://dx.doi.org/10.1107/s2052520617014299.

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Co-crystallization experiments conducted between ortho-phenylenediamine (OPDA) and five substituted aromatic acids (phthalic acid, salicylic acid, 4-hydroxybenzoic acid, 4-nitrobenzoic acid and 3,5-dinitrobenzoic acid) reveal the formation of supramolecular networks constructed from acid–base heterosynthons of ortho-phenylenediammonium cations with respective aromatic anions. All of these coformers are generally regarded as safe (GRAS) molecules. The five reported crystal structures are sustained predominantly by intermolecular N+−H...O−, N—H...O− and N—H...O hydrogen-bonding interactions; in addition intramolecular O—H...O and intermolecular O—H...O, O—H...O− and C—H...O interactions contribute to the formation of various networks. Five 1:1 salts [NH2C6H4NH3]+·[COOHC6H4COO]− (1); [NH2C6H4NH3]+·[OHC6H4COO]− (2); [{NH2C6H4NH2}2·{OHC6H4COOH}2·{NH2C6H4NH3}+ 2·{OHC6H4COO}− 2] (OPDPHB) (3); [NH2C6H4NH3]+·[NO2C6H4COO]− (4) and [NH2C6H4NH3]+·[(NO2)2C6H4COO]− (5) were isolated as single crystals by the slow evaporation method and were characterized using spectroscopic and X-ray crystallographic techniques. X-ray diffraction studies confirmed the formation of salts. The pK a difference between the amine and respective acid favours the transfer of a proton from the acid to the amine, which leads to the formation of the anion and the cation. The interactions between these ions resulted in a stable heterosynthon in each case. The asymmetric units of salts (1), (2), (4) and (5) contain one anion and one cation each, but salt (3) consists of two anions, two cations and two neutral species in its asymmetric unit. A polymorph of salt (3) was also isolated from the crystallization of the ground material from liquid-assisted grinding [{NH2C6H4NH2}·{NH2C6H4NH3}+·{OHC6H4COO}−] (OPDPHB 3P). The polymorph crystallized in the monoclinic non-centrosymmetric space group P21. The liquid-assisted grinding experiments using a 1:1 ratio also revealed the formation of the expected salts, except salt (3), where this product matches with polymorph (OPDPHB 3P).

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Wu, Huaiguang, Pengjie Xie, Huiyi Zhang, Daiyi Li, and Ming Cheng. "Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 2893–907. http://dx.doi.org/10.3233/jifs-191438.

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The chest X-ray examination is one of the most important methods for screening and diagnosing of many lung diseases. Diagnosis of pneumonia by chest X-ray is one of the common methods used by medical experts. However, the image quality of chest X-Ray has some defects, such as low contrast, overlapping organs and blurred boundary, which seriously affects detecting pneumonia in chest X-rays. Therefore, it has important medical value and application significance to construct a stable and accurate automatic detection model of pneumonia through a large number of chest X-ray images. In this paper, we propose a novel hybrid system for detecting pneumonia from chest X-Ray image: ACNN-RF, which is an adaptive median filter Convolutional Neural Network (CNN) recognition model based on Random forest (RF). Firstly, the improved adaptive median filtering is employed to remove noise in the chest X-ray image, which makes the image more easily recognized. Secondly, we establish the CNN architecture based on Dropout to extract deep activation features from each chest X-ray image. Finally, we employ the RF classifier based on GridSearchCV class as a classifier for deep activation features in CNN model. It not only avoids the phenomenon of over-fitting in data training, but also improves the accuracy of image classification. During our experiment, the public chest X-ray image dataset used in the experiment contains 5863 images, which comprises 4265 frontal-view X-ray images of 1574 unique patients. The average recognition rate of pneumonia is up to 97% by the proposed ACNN-RF. The experimental results show that the ACNN-RF identification system is more effective than the previous traditional image identification system.

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Ahn, Jin-Ho, Won-Jae Jang, Won-Hee Lee, and Jeong-Do Kim. "Detection of Needles in Meat using X-Ray Images and Convolution Neural Networks." JOURNAL OF SENSOR SCIENCE AND TECHNOLOGY 29, no. 6 (2020): 427–32. http://dx.doi.org/10.46670/jsst.2020.29.6.427.

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Zhang, Alan. "Covid-19 Chest X-ray Images: Lung Segmentation and Diagnosis using Neural Networks." International Journal on Computational Science & Applications 10, no. 5 (2020): 1–11. http://dx.doi.org/10.5121/ijcsa.2020.10501.

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COVID-19 has caused world-wide disturbances and the machine learning community has been finding ways to combat the disease. Applications of neural networks in image processing tasks allow COVID-19 Chest X-ray images to be meaningfully processed. In this study, the V7 Darwin COVID-19 Chest X-ray Dataset is used to train a U-Net based network that performs lung-region segmentation and a convolutional neural network that performs diagnosis on Chest X-ray images. This dataset is larger than most of the datasets used to develop existing COVID-19 related neural networks. The lung segmentation network achieved an accuracy of 0.9697 on the training set and an accuracy of 0.9575, an Intersectionover-union of 0.8666, and a dice coefficient of 0.9273 on the validation set. The diagnosis network achieved an accuracy of 0.9620 on the training set and an accuracy of 0.9666 and AUC of 0.985 on the validation set.

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Plautz, Tia, Rosanne Boudreau, Jian-Hua Chen, et al. "Progress Toward Automatic Segmentation of Soft X-ray Tomograms Using Convolutional Neural Networks." Microscopy and Microanalysis 23, S1 (2017): 984–85. http://dx.doi.org/10.1017/s143192761700558x.

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Casasent, David, and Xue-wen Chen. "New training strategies for RBF neural networks for X-ray agricultural product inspection." Pattern Recognition 36, no. 2 (2003): 535–47. http://dx.doi.org/10.1016/s0031-3203(02)00058-4.

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Kosiba, Matej, Maggie Lieu, Bruno Altieri, et al. "Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks." Monthly Notices of the Royal Astronomical Society 496, no. 4 (2020): 4141–53. http://dx.doi.org/10.1093/mnras/staa1723.

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ABSTRACT Galaxy clusters appear as extended sources in XMM–Newton images, but not all extended sources are clusters. So, their proper classification requires visual inspection with optical images, which is a slow process with biases that are almost impossible to model. We tackle this problem with a novel approach, using convolutional neural networks (CNNs), a state-of-the-art image classification tool, for automatic classification of galaxy cluster candidates. We train the networks on combined XMM–Newton X-ray observations with their optical counterparts from the all-sky Digitized Sky Survey. Our data set originates from the XMM CLuster Archive Super Survey (X-CLASS) survey sample of galaxy cluster candidates, selected by a specially developed pipeline, the XAmin, tailored for extended source detection and characterization. Our data set contains 1707 galaxy cluster candidates classified by experts. Additionally, we create an official Zooniverse citizen science project, The Hunt for Galaxy Clusters, to probe whether citizen volunteers could help in a challenging task of galaxy cluster visual confirmation. The project contained 1600 galaxy cluster candidates in total of which 404 overlap with the expert’s sample. The networks were trained on expert and Zooniverse data separately. The CNN test sample contains 85 spectroscopically confirmed clusters and 85 non-clusters that appear in both data sets. Our custom network achieved the best performance in the binary classification of clusters and non-clusters, acquiring accuracy of 90 per cent, averaged after 10 runs. The results of using CNNs on combined X-ray and optical data for galaxy cluster candidate classification are encouraging, and there is a lot of potential for future usage and improvements.

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Mohan, Arun Prasad. "Deep Convolutional Neural Networks in Detecting Lung Mass From Chest X-Ray Images." International Journal of Applied Research in Bioinformatics 11, no. 1 (2021): 22–30. http://dx.doi.org/10.4018/ijarb.2021010103.

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There are more than one million cases of lung cancer per year in India alone. Early detection is vital in increasing the survival rate and decreasing treatment costs. This research is aimed at building a deep convolutional neural network which uses chest x-rays to identify lung mass, and then make a comparative study by tuning the hyperparameters. NIH Chest X-Ray Dataset containing more than 112,000 images were used for training and testing. The data was analysed and then fed to the neural network. Accuracy of over 96% was obtained in all the trials. A comparative study by varying the number of inputs and varying the number of hidden layers was carried out. The accuracies obtained were compared and was found that the accuracy increased with the increase in the number of hidden layers. A complete product was then ideated which when implemented would be a vital diagnostic tool and can be used in the remote locations of a country having just x-ray facilities and no other advanced medical equipment like CT.

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Sekeroglu, Boran, and Ilker Ozsahin. "Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks." SLAS TECHNOLOGY: Translating Life Sciences Innovation 25, no. 6 (2020): 553–65. http://dx.doi.org/10.1177/2472630320958376.

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The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics–area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images.

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Santos, Adam, Raimundo Neto, Victor Souza, Leandro Araújo, and Luan Silva. "Convolutional neural networks applied in the detection of pneumonia by x-ray images." International Journal of Innovative Computing and Applications 13, no. 4 (2022): 1. http://dx.doi.org/10.1504/ijica.2022.10039108.

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Sukegawa, Shintaro, Kazumasa Yoshii, Takeshi Hara, et al. "Deep Neural Networks for Dental Implant System Classification." Biomolecules 10, no. 7 (2020): 984. http://dx.doi.org/10.3390/biom10070984.

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In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.

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Lee, Kwang Ming, Hai-Chou Chang, Jyh-Chiang Jiang та ін. "C−H- - -O Hydrogen Bonds in β-Sheetlike Networks: Combined X-ray Crystallography and High-Pressure Infrared Study". Journal of the American Chemical Society 125, № 40 (2003): 12358–64. http://dx.doi.org/10.1021/ja036719z.

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Madkhali, Marwah M. M., Conor D. Rankine, and Thomas J. Penfold. "Enhancing the analysis of disorder in X-ray absorption spectra: application of deep neural networks to T-jump-X-ray probe experiments." Physical Chemistry Chemical Physics 23, no. 15 (2021): 9259–69. http://dx.doi.org/10.1039/d0cp06244h.

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Gándara, Felipe, and Thomas D. Bennett. "Crystallography of metal–organic frameworks." IUCrJ 1, no. 6 (2014): 563–70. http://dx.doi.org/10.1107/s2052252514020351.

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Metal–organic frameworks (MOFs) are one of the most intensely studied material types in recent times. Their networks, resulting from the formation of strong bonds between inorganic and organic building units, offer unparalled chemical diversity and pore environments of growing complexity. Therefore, advances in single-crystal X-ray diffraction equipment and techniques are required to characterize materials with increasingly larger surface areas, and more complex linkers. In addition, whilst structure solution from powder diffraction data is possible, the area is much less populated and we detail the current efforts going on here. We also review the growing number of reports on diffraction under non-ambient conditions, including the response of MOF structures to very high pressures. Such experiments are important due to the expected presence of stresses in proposed applications of MOFs – evidence suggesting rich and complex behaviour. Given the entwined and inseparable nature of their structure, properties and applications, it is essential that the field of structural elucidation is able to continue growing and advancing, so as not to provide a rate-limiting step on characterization of their properties and incorporation into devices and applications. This review has been prepared with this in mind.

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Novitasari, Dian, Hironari Kamikubo, Yoichi Yamazaki, Mariko Yamaguchi, and Mikio Kataoka. "Excited-State Proton Transfer in Fluorescent Photoactive Yellow Protein Containing 7-Hydroxycoumarin." Advanced Materials Research 896 (February 2014): 85–88. http://dx.doi.org/10.4028/www.scientific.net/amr.896.85.

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Green fluorescent protein (GFP) has been used as an effective tool in various biological fields. The large Stokes shift resulting from an excited-state proton transfer (ESPT) is the basis for the application of GFP in such techniques as ratiometric GFP biosensors. The chromophore of GFP is known to be involved in a hydrogen-bonding network. Previous X-ray crystallographic and FTIR studies suggest that a proton wire along the hydrogen-bonding network plays a role in the ESPT. In order to examine the relationship between the ESPT and hydrogen-bonding network within proteins, we prepared an artificial fluorescent protein using a light-sensor protein, photoactive yellow protein (PYP). The native chromophore of p-coumaric acid (pCA) of PYP undergoes trans-cis isomerization after absorbing a photon, which triggers proton transfers within the hydrogen-bonding network comprised of pCA and proximal amino acid residues. Although PYP emits little fluorescence, we succeeded to reconstitute an artificial fluorescent PYP (PYP-coumarin) by substituting the pCA with its trans-lock analog 7-hydroxycoumarin. Spectroscopic studies with PYP-coumarin revealed that the chromophore takes an anionic form at neutral pH, but is protonated by lowering pH. Both the protonated and deprotonated forms of PYP-coumarin emit intense fluorescence, as compared with the native PYP. In addition, both the deprotonated and protonated forms show identical λmax values in their fluorescence spectra, indicating that ESPT occurs in the artificial fluorescent protein.

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Liu, Shuai, Charles N. Melton, Singanallur Venkatakrishnan, et al. "Convolutional neural networks for grazing incidence x-ray scattering patterns: thin film structure identification." MRS Communications 9, no. 02 (2019): 586–92. http://dx.doi.org/10.1557/mrc.2019.26.

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Chen, Hsin-Jui, Shanq-Jang Ruan, Sha-Wo Huang, and Yan-Tsung Peng. "Lung X-ray Segmentation using Deep Convolutional Neural Networks on Contrast-Enhanced Binarized Images." Mathematics 8, no. 4 (2020): 545. http://dx.doi.org/10.3390/math8040545.

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Automatically locating the lung regions effectively and efficiently in digital chest X-ray (CXR) images is important in computer-aided diagnosis. In this paper, we propose an adaptive pre-processing approach for segmenting the lung regions from CXR images using convolutional neural networks-based (CNN-based) architectures. It is comprised of three steps. First, a contrast enhancement method specifically designed for CXR images is adopted. Second, adaptive image binarization is applied to CXR images to separate the image foreground and background. Third, CNN-based architectures are trained on the binarized images for image segmentation. The experimental results show that the proposed pre-processing approach is applicable and effective to various CNN-based architectures and can achieve comparable segmentation accuracy to that of state-of-the-art methods while greatly expediting the model training by up to 20.74 % and reducing storage space for CRX image datasets by down to 94.6 % on average.

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Ruisanchez, I., P. Potokar, J. Zupan, and V. Smolej. "Classification of Energy Dispersion X-ray Spectra of Mineralogical Samples by Artificial Neural Networks†." Journal of Chemical Information and Computer Sciences 36, no. 2 (1996): 214–20. http://dx.doi.org/10.1021/ci950068b.

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Jain, Rachna, Preeti Nagrath, Gaurav Kataria, V. Sirish Kaushik, and D. Jude Hemanth. "Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning." Measurement 165 (December 2020): 108046. http://dx.doi.org/10.1016/j.measurement.2020.108046.

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Fuliang Wang and Feng Wang. "Void Detection in TSVs With X-Ray Image Multithreshold Segmentation and Artificial Neural Networks." IEEE Transactions on Components, Packaging and Manufacturing Technology 4, no. 7 (2014): 1245–50. http://dx.doi.org/10.1109/tcpmt.2014.2322907.

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