Characterization and Classification of Pores in Metal 3D Printing Materials with X-ray Tomography and Machine Learning

BACKGROUND: Cancer is a major health problem not only in Indonesia but also throughout the world. Cancer is the growth and spread of abnormal cells that have distinctive characteristics, that if can no longer be controlled will usually cause death. The number of deaths due to cancer is generally caused by late diagnosis and inappropriate treatment. To reduce mortality from cancer, it is necessary to strive for early detection and monitoring of cancer in patients undergoing therapy. Convolutional neural networks (CNNs) as one of machine learning methods are designed to produce or process data from two dimensions that have a network tier and many applications carried out in the image. Moreover, support vector machines (SVMs) as a hypothetical space in the form of linear functions feature have high dimensions and trained algorithm based on optimization theory. AIM: In connection with the above, this paper discusses the role of the machine learning technique named a hybrid CNN-SVM. METHODS: The proposed method is used in the detection and monitoring of cancers by determining the classification of cancers in X-ray computed tomography (CT) patients’ images. Several types of cancer that used for determination in detection and monitoring of cancers diagnosis are also discussed in this paper, such as lung, liver, and breast cancer. RESULTS: From the discussion, the results show that the combining model of hybrid CNN-SVM has the best performance with 99.17% accuracy value. CONCLUSION: Therefore, it can be concluded that machine learning plays a very important role in the detection and management of cancer treatment through the determination of classification of cancers in X-ray CT patients’ images. As the proposed method can detect cancer cells with an effective mechanism of action so can has the potential to inhibit in the future studies with more extensive data materials and various diseases.

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COVID-19 Classification of X-ray Images Using Deep Neural Networks

10.1101/2020.10.01.20204073 ◽

2020 ◽

Author(s):

Elisha Goldstein ◽

Daphna Keidar ◽

Daniel Yaron ◽

Yair Shachar ◽

Ayelet Blass ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Receiver Operating Characteristic ◽

Deep Neural Networks ◽

Operating Characteristic ◽

Learning Model ◽

X Ray ◽

Machine Learning Model ◽

Chest X Ray

AbstractBackgroundIn the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in the diagnosis and monitoring of patients with COVID-19. Machine learning solutions have been shown to be useful for X-ray analysis and classification in a range of medical contexts.PurposeThe purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans.Materials and MethodsIn this retrospective study, a classifier was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation to detect COVID-19 in frontal CXR images collected between January 2018 and July 2020 in four hospitals in Israel. A nearest-neighbors algorithm was implemented based on the network results that identifies the images most similar to a given image. The model was evaluated using accuracy, sensitivity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and of the precision-recall (P-R) curve.ResultsThe dataset sourced for this study includes 2362 CXRs, balanced for positive and negative COVID-19, from 1384 patients (63 +/- 18 years, 552 men). Our model achieved 89.7% (314/350) accuracy and 87.1% (156/179) sensitivity in classification of COVID-19 on a test dataset comprising 15% (350 of 2326) of the original data, with AUC of ROC 0.95 and AUC of the P-R curve 0.94. For each image we retrieve images with the most similar DNN-based image embeddings; these can be used to compare with previous cases.ConclusionDeep Neural Networks can be used to reliably classify CXR images as COVID-19 positive or negative. Moreover, the image embeddings learned by the network can be used to retrieve images with similar lung findings.SummaryDeep Neural Networks and can be used to reliably predict chest X-ray images as positive for coronavirus disease 2019 (COVID-19) or as negative for COVID-19.Key ResultsA machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for coronavirus disease 2019 with accuracy of 89.7%, sensitivity of 87.1% and area under receiver operating characteristic curve of 0.95.A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model’s image embeddings.

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Universal scaling laws of keyhole stability and porosity in 3D printing of metals

10.21203/rs.3.rs-94648/v1 ◽

2020 ◽

Author(s):

Zhengtao Gan ◽

Orion Kafka ◽

Niranjan Parab ◽

Cang Zhao ◽

Lichao Fang ◽

...

Keyword(s):

3D Printing ◽

Scaling Laws ◽

Three Dimensional ◽

Dimensionless Number ◽

Multiphysics Modeling ◽

X Ray ◽

Universal Scaling ◽

X Ray Imaging ◽

Metal 3D Printing ◽

Defect Elimination

Abstract We leverage ultrahigh-speed synchrotron x-ray imaging and high-fidelity multiphysics modeling to identify strikingly simple yet universal scaling laws for keyhole stability and porosity in metal three-dimensional (3D) printing. The laws apply broadly and remain accurate for different materials, processing conditions, and printing machines. We define a new dimensionless number, the Keyhole number, to predict aspect ratio of a keyhole and the morphological transition from stable at low Keyhole number to chaotic at high Keyhole number. Furthermore, we discover inherent correlation between keyhole stability and porosity formation in metal 3D printing. By reducing the dimensions of the formulation of these challenging problems, the compact scaling laws will aid process optimization and defect elimination during metal 3D printing, and potentially lead to a quantitative predictive framework.

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Classification of chest X-ray images using Machine Learning and Histogram of Oriented Gradients

10.5753/eniac.2021.18240 ◽

2021 ◽

Author(s):

Fellipe M. C. Barbosa ◽

Anne Magaly de P. Canuto

Keyword(s):

Machine Learning ◽

Histogram Of Oriented Gradients ◽

X Ray ◽

Chest X Ray

Este trabalho propõe um modelo de aprendizado de máquina para classificar e detectar a presença de pneumonia a partir de uma coleção de amostras de radiografias do tórax. Ao contrário da maioria dos trabalhos que utilizam abordagens de aprendizado profundo para classificar se a imagem é de um pulmão com pneumonia ou não, ou seja, duas classes para assim alcançar um desempenho de classificação notável, este modelo utiliza Histograma de Gradientes Orientados para extrair características de uma determinada imagem de raio-X de tórax e classificá-la em três classes, determinando se uma pessoa está ou não infectada com pneumonia viral ou bacteriana. Apesar de uma maior complexidade e utilização de modelos tradicionais de aprendizado de máquina, a maior acurácia alcançada foi de 91.32% superior a de trabalhos que utilizam redes profundas e buscam resolver o mesmo grau de complexidade.

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Macro-classification of meteorites by portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF), principal component analysis (PCA) and machine learning algorithms

Talanta ◽

10.1016/j.talanta.2020.120785 ◽

2020 ◽

Vol 212 ◽

pp. 120785 ◽

Cited By ~ 8

Author(s):

Ignazio Allegretta ◽

Bruno Marangoni ◽

Paola Manzari ◽

Carlo Porfido ◽

Roberto Terzano ◽

...

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Fluorescence Spectroscopy ◽

Learning Algorithms ◽

Principal Component ◽

Machine Learning Algorithms ◽

Energy Dispersive ◽

X Ray ◽

Classification Of Meteorites

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A machine learning approach for classification of accretion states of black hole binaries

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab151 ◽

2021 ◽

Vol 502 (1) ◽

pp. 1334-1343

Author(s):

H Sreehari ◽

Anuj Nandi

Keyword(s):

Machine Learning ◽

Black Hole ◽

Hierarchical Clustering ◽

Clustering Algorithms ◽

Machine Learning Algorithms ◽

X Ray ◽

Black Hole Binaries ◽

Multiple Parameters ◽

Machine Learning Approach

ABSTRACT In this paper, we employ Machine Learning algorithms on multimission observations for the classification of accretion states of outbursting black hole X-ray binaries for the first time. Archival data from RXTE, Swift, MAXI, and AstroSat observatories are used to generate the hardness intensity diagrams (HIDs) for outbursts of the sources XTE J1859+226 (1999 outburst), GX 339−4 (2002, 2004, 2007, and 2010 outbursts), IGR J17091−3624 (2016 outburst), and MAXI J1535−571 (2017 outburst). Based on variation of X-ray flux, hardness ratios, presence of various types of quasi-periodic oscillations (QPOs), photon indices, and disc temperature, we apply clustering algorithms like K-Means clustering and Hierarchical clustering to classify the accretion states (clusters) of each outburst. As multiple parameters are involved in the classification process, we show that clustering algorithms club together the observations of similar characteristics more efficiently than the ‘standard’ method of classification. We also infer that K-Means clustering provides more reliable results than Hierarchical clustering. We demonstrate the importance of the classification based on machine learning by comparing it with results from ‘standard’ classification.

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Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans

Iraqi Journal of Science ◽

10.24996/ijs.2021.62.6.35 ◽

2021 ◽

pp. 2099-2109

Author(s):

Maad M. Mijwil

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Excellent Performance ◽

Wuhan City ◽

X Ray ◽

Learning Techniques ◽

Chest X Ray ◽

The Common

COVID-19 (Coronavirus disease-2019), commonly called Coronavirus or CoV, is a dangerous disease caused by the SARS-CoV-2 virus. It is one of the most widespread zoonotic diseases around the world, which started from one of the wet markets in Wuhan city. Its symptoms are similar to those of the common flu, including cough, fever, muscle pain, shortness of breath, and fatigue. This article suggests implementing machine learning techniques (Random Forest, Logistic Regression, Naïve Bayes, Support Vector Machine) by Python to classify a series of chest X-ray images that include viral pneumonia, COVID-19, and healthy (Not infected) cases in humans. The study includes more than 1400 images that are collected from the Kaggle platform. The experimental outcomes of this study confirmed that the supported vector machine technique has high accuracy and excellent performance in the classification of the disease, as reflected by values of 91.8% accuracy, 91.7% sensitivity, 95.9% specificity, 91.8% F1-score, and 97.6% AUC.

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Classification of Chest X-ray Images Using Machine Learning Techniques

10.20944/preprints202103.0408.v1 ◽

2021 ◽

Author(s):

Abdelmoty Ahmed ◽

Gamal Tharwat ◽

Belgacem Bouallegue ◽

Mahmoud Khattab ◽

Ahmad Al Moustafa ◽

...

Keyword(s):

Machine Learning ◽

Activation Function ◽

Machine Learning Techniques ◽

Mean Square ◽

Classification Result ◽

X Ray ◽

Number Of Layers ◽

Learning Techniques ◽

Chest X Ray

Machine Learning has completely transformed health care system, which transmits medical data through IOT sensors. So it is very important to encrypt them to protect patient data. encrypting medical images from a performance perspective consumes time; hence the use of an auto encoder is essential. An auto encoder is used in this work to compress the image as a vector prior to the encryption process. The digital image passes across description function and a decoder to get back the image in the proposed work; various experiments are carried out on hyper parameters to achieve the highest outcome of the classification. The findings demonstrate that the combination of Mean Square Logarithmic Error as the loss function, ADA grad as an optimizer, two layers for the encoder, and another reverse for the decoder, RELU as the activation function generates the best auto encoder results. The combination of Mean square error (lose function), RMS prop (optimizer), three layers for the encoder and another reverse for the decoder, and RELU (activation function) has the best classification result. All the experiments with different hyper parameter has run almost very close to each other even when changing the number of layers. The running time is between 9 and 16 second for each epoch.

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