scholarly journals A Simple Method for Automatic 3D Reconstruction of Coronary Arteries From X-Ray Angiography

2021 ◽  
Vol 12 ◽  
Author(s):  
Minki Hwang ◽  
Sa-Bin Hwang ◽  
Hyosang Yu ◽  
Jaehyeok Kim ◽  
Daehyun Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Arteries ◽  
Distal Region ◽  
Proximal Region ◽  
Automatic Identification ◽  
Success Rates ◽  
Simple Method ◽  
X Ray ◽  
Learning Network ◽  
Deep Learning Network

Automatic three-dimensional (3-D) reconstruction of the coronary arteries (CA) from medical imaging modalities is still a challenging task. In this study, we present a deep learning-based method of automatic identification of the two ends of the vessel from X-ray coronary angiography (XCA). We also present a method of using template models of CA in matching the two-dimensional segmented vessels from two different angles of XCA. For the deep learning network, we used a U-net consisting of an encoder (Resnet) and a decoder. The two ends of the vessel were manually labeled to generate training images. The network was trained with 2,342, 1,907, and 1,523 labeled images for the left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA), respectively. For template models of CA, ten reconstructed 3-D models were averaged for each artery. The accuracy of correspondence using template models was compared with that of manual matching. The deep learning network pointed the proximal region (20% of the total length) in 97.7, 97.5, and 96.4% of 315, 201, and 167 test images for LAD, LCX, and RCA, respectively. The success rates in pointing the distal region were 94.9, 89.8, and 94.6%, respectively. The average distances between the projected points from the reconstructed 3-D model to the detector and the points on the segmented vessels were not statistically different between the template and manual matchings. The computed FFR was not significantly different between the two matchings either. Deep learning methodology is feasible in identifying the two ends of the vessel in XCA, and the accuracy of using template models is comparable to that of manual correspondence in matching the segmented vessels from two angles.

scholarly journals A bagging dynamic deep learning network for diagnosing COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhijun Zhang ◽  
Bozhao Chen ◽  
Jiansheng Sun ◽  
Yamei Luo
Keyword(s):  
Deep Learning ◽  
Chest Radiography ◽  
Learning Algorithm ◽  
Dynamic Learning ◽  
X Ray ◽  
Learning Network ◽  
Feature Extractor ◽  
In Series ◽  
Image Set ◽  
Deep Learning Network

AbstractCOVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN) for diagnosing COVID-19 by intelligently recognizing its symptoms in X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, a bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects the feature extractor and bagging classifier in series. Experimental results verify that the proposed B-DDLN achieves 98.8889% testing accuracy, which shows the best diagnosis performance among the existing state-of-the-art methods on the open image set. It also provides evidence for further detection and treatment.

scholarly journals Recognition of corona virus disease (COVID-19) using deep learning network

2021 ◽  
Vol 11 (1) ◽  
pp. 365
Author(s):  
Ashwan A. Abdulmunem ◽  
Zinah Abdulridha Abutiheen ◽  
Hiba J. Aleqabie
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Virus Disease ◽  
Learning Networks ◽  
Accuracy Rate ◽  
X Ray ◽  
Infected People ◽  
Learning Network ◽  
Corona Virus ◽  
Deep Learning Network

Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.

scholarly journals A Bagging Dynamic Deep Learning Network for Diagnosing COVID-19

2021 ◽  
Author(s):  
Zhijun Zhang ◽  
Bozhao Chen ◽  
Jiansheng Sun
Keyword(s):  
Deep Learning ◽  
Chest Radiography ◽  
Learning Algorithm ◽  
Dynamic Learning ◽  
X Ray ◽  
Learning Network ◽  
Feature Extractor ◽  
In Series ◽  
Image Set ◽  
Deep Learning Network

Abstract COVID-19 is a serious epidemic all over the world. As an efficient way in intelligent medical services, using X-ray chest radiography image for automatically diagnosing COVID-19 provides huge assistances and conveniences for clinicians in practice. In this paper, a bagging dynamic deep learning network (B-DDLN) is proposed for diagnosing COVID-19 by intelligently recognizing X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects feature extractor and bagging classifier in series. Experimental results verify that using the proposed B-DDLN can achieve 98.8889% testing accuracy, which illustrates the best diagnosis performances among existing methods on the open image set and provides evidences for further detection and treatment.

scholarly journals Overview of Deep Learning Models in Biomedical Domain with the Help of R Statistical Software

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Vinaitheerthan Renganathan
Keyword(s):  
Deep Learning ◽  
Unstructured Data ◽  
Biomedical Domain ◽  
Learning Networks ◽  
Learning Models ◽  
Statistical Software ◽  
X Ray ◽  
Learning Network ◽  
Building Models ◽  
Deep Learning Network

Abstract With the increase in volume of data and presence of structured and unstructured data in the biomedical filed, there is a need for building models which can handle complex & non-linear relations in the data and also predict and classify outcomes with higher accuracy. Deep learning models are one of such models which can handle complex and nonlinear data and are being increasingly used in the biomedical filed in the recent years. Deep learning methodology evolved from artificial neural networks which process the input data through multiple hidden layers with higher level of abstraction. Deep Learning networks are used in various fields such as image processing, speech recognition, fraud deduction, classification and prediction. Objectives of this paper is to provide an overview of Deep Learning Models and its application in the biomedical domain using R Statistical software Deep Learning concepts are illustrated by using the R statistical software package. X-ray Images from NIH datasets used to explain the prediction accuracy of the deep learning models. Deep Learning models helped to classify the outcomes under study with 91% accuracy. The paper provided an overview of Deep Learning Models, its types, its application in biomedical domain. This paper has shown the effect of deep learning network in classifying images into normal and disease with 91% accuracy with help of the R statistical package.

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

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