scholarly journals Automatic Quality Control in Lung X-Ray Imaging with Deep Learning

2021 ◽  
Author(s):  
A. A. Dovganich ◽  
A. V. Khvostikov ◽  
A. S. Krylov ◽  
L. E. Parolina
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
X Ray ◽  
X Ray Imaging ◽  
Automatic Quality Control

scholarly journals Deep learning based solder joint defect detection on industrial printed circuit board X-ray images

2022 ◽  
Author(s):  
Qianru Zhang ◽  
Meng Zhang ◽  
Chinthaka Gamanayake ◽  
Chau Yuen ◽  
Zehao Geng ◽  
...  
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Defect Detection ◽  
Printed Circuit Board ◽  
Circuit Board ◽  
Image Size ◽  
X Ray ◽  
Component Size ◽  
Image Dimension ◽  
X Ray Imaging

AbstractWith the improvement of electronic circuit production methods, such as reduction of component size and the increase of component density, the risk of defects is increasing in the production line. Many techniques have been incorporated to check for failed solder joints, such as X-ray imaging, optical imaging and thermal imaging, among which X-ray imaging can inspect external and internal defects. However, some advanced algorithms are not accurate enough to meet the requirements of quality control. A lot of manual inspection is required that increases the specialist workload. In addition, automatic X-ray inspection could produce incorrect region of interests that deteriorates the defect detection. The high-dimensionality of X-ray images and changes in image size also pose challenges to detection algorithms. Recently, the latest advances in deep learning provide inspiration for image-based tasks and are competitive with human level. In this work, deep learning is introduced in the inspection for quality control. Four joint defect detection models based on artificial intelligence are proposed and compared. The noisy ROI and the change of image dimension problems are addressed. The effectiveness of the proposed models is verified by experiments on real-world 3D X-ray dataset, which saves the specialist inspection workload greatly.

scholarly journals Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging

2020 ◽  
Vol 7 ◽  
Author(s):  
Seung Hoon Yoo ◽  
Hui Geng ◽  
Tin Lok Chiu ◽  
Siu Ki Yu ◽  
Dae Chul Cho ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Tree ◽  
Decision Tree Classifier ◽  
X Ray ◽  
X Ray Imaging ◽  
Tree Classifier ◽  
Chest X Ray

scholarly journals Quality management systems in radiology

2013 ◽  
Vol 17 (3) ◽  
pp. 84-88
Author(s):  
Geoffrey K. Korir ◽  
Jeska S. Wambani ◽  
Ian K. Korir ◽  
Mark Tries ◽  
Beatrice M. Mulama
Keyword(s):  
Quality Control ◽  
Quality Management ◽  
Management Systems ◽  
Reference Level ◽  
Quality Management Systems ◽  
X Ray ◽  
Dose Monitoring ◽  
X Ray Imaging ◽  
National Quality ◽  
Medical Facilities

Background: The use of X-ray imaging is ever increasing in proportion to the need for radiological services and technological capabilities. Quality management that includes patient radiation dose monitoring is fundamental to safety and quality improvement of radiological services.Objective: To assess the level of quality management systems in X-ray medical facilities in Kenya.Methods: Quality management inspection, quality control performance tests and patient radiation exposure were assessed in 54 representative X-ray medical facilities. Additionally, a survey of X-ray examination frequency was conducted in 140 hospitals across the country.Results: The overall findings placed the country’s X-ray imaging quality management systems at 61±3% out of a possible 100%. The most and the least quality assurance performance indicators were general radiography X-ray equipment quality control tests at 88±4%, and the interventional cardiology adult examinations below diagnostic reference level at 25±1%, respectively.Conclusions: The study used a systematic evidence-based approach for the assessment of national quality management systems in radiological practice in clinical application, technical conduct of the procedure, image quality criteria, and patient characteristics as part of the quality management programme.

scholarly journals SADA communique to members - Licence and quality control tests for dental diagnostic X-ray imaging systems

2020 ◽  
Vol 75 (10) ◽  
pp. 531
Author(s):  
SADA Head Office
Keyword(s):  
Quality Control ◽  
Legal Framework ◽  
Imaging Systems ◽  
X Ray ◽  
Code Of Practice ◽  
Radiation Control ◽  
Legal Standing ◽  
X Ray Imaging ◽  
Legislative Framework ◽  
Erroneous Data

Members have been receiving notification from Inspection Bodies about their X-ray equipment being due for inspection. On enquiry, it has been discovered that, in some instances, this notice was based on erroneous data that Inspection bodies may have received from the Radiation Control, who had not correctly updated their records, due to "a lack of manpower". SADA strongly advises members who receive this notification to check their documentation before booking said inspections and, if the notification is incorrect, to request their usual inspection body to correct their records. SADA has serious concerns about the current legal framework and enforcement of the Act governing X-ray equipment. It questions the Code of Practice and more particularly, the legal standing of inspection bodies, licensing delays and the entire legislative framework. To this end, SADA has made an extensive written submission to the Minister of Health raising our concerns. In the meantime, as the legislative framework applies to practitioner, in its flawed framework, we provide members useful information for the benefit of members around the whole issue of licensing and testing of X-ray equipment so that members are properly informed when receiving notices of inspections or having to licence their new or used X-ray equipment.

scholarly journals Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mundher Mohammed Taresh ◽  
Ningbo Zhu ◽  
Talal Ahmed Ali Ali ◽  
Asaad Shakir Hameed ◽  
Modhi Lafta Mutar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Detection Accuracy ◽  
X Rays ◽  
X Ray ◽  
Specificity And Sensitivity ◽  
X Ray Imaging ◽  
Novel Coronavirus

The novel coronavirus disease 2019 (COVID-19) is a contagious disease that has caused thousands of deaths and infected millions worldwide. Thus, various technologies that allow for the fast detection of COVID-19 infections with high accuracy can offer healthcare professionals much-needed help. This study is aimed at evaluating the effectiveness of the state-of-the-art pretrained Convolutional Neural Networks (CNNs) on the automatic diagnosis of COVID-19 from chest X-rays (CXRs). The dataset used in the experiments consists of 1200 CXR images from individuals with COVID-19, 1345 CXR images from individuals with viral pneumonia, and 1341 CXR images from healthy individuals. In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pretrained deep learning algorithms and fine-tuned to maximise detection accuracy to identify the best algorithms. The results showed that deep learning with X-ray imaging is useful in collecting critical biological markers associated with COVID-19 infections. VGG16 and MobileNet obtained the highest accuracy of 98.28%. However, VGG16 outperformed all other models in COVID-19 detection with an accuracy, F1 score, precision, specificity, and sensitivity of 98.72%, 97.59%, 96.43%, 98.70%, and 98.78%, respectively. The outstanding performance of these pretrained models can significantly improve the speed and accuracy of COVID-19 diagnosis. However, a larger dataset of COVID-19 X-ray images is required for a more accurate and reliable identification of COVID-19 infections when using deep transfer learning. This would be extremely beneficial in this pandemic when the disease burden and the need for preventive measures are in conflict with the currently available resources.

Mobile x-ray apparatus for automatic quality control of single crystals

2014 ◽  
Author(s):  
Nikolay N. Potrakhov ◽  
Viktor V. Bessonov ◽  
Anatoliy V. Obodovskiy ◽  
Viktor A. Lifshits ◽  
Ramon E. Oses
Keyword(s):  
Quality Control ◽  
Single Crystals ◽  
X Ray ◽  
Automatic Quality Control

scholarly journals A Review on Deep Learning Approaches for COVID-19 Detection in Chest X-Ray Images

2021 ◽  
Vol 9 (10) ◽  
pp. 1792-1801
Author(s):  
Tanishka Dodiya
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
High Rate ◽  
Learning Approaches ◽  
X Ray ◽  
First Case ◽  
X Ray Imaging ◽  
The World ◽  
Chest X Ray ◽  
Diagnostic Capabilities

Abstract: COVID-19 also famously known as Coronavirus is one of the deadliest viruses found in the world, which has a high rate in both demise and spread. This has caused a severe pandemic in the world. The virus was first reported in Wuhan, China, registering causes like pneumonia. The first case was encountered on December 31, 2019. As of 20th October 2021, more than 242 million cases have been reported in more than 188 countries, and it has around 5 million deaths. COVID- 19 infected persons have pneumonia-like symptoms, and the infection damages the body's respiratory organs, making breathing difficult. The elemental clinical equipment as of now being employed for the analysis of COVID-19 is RT-PCR, which is costly, touchy, and requires specific clinical workforce. According to recent studies, chest X-ray scans include important information about the start of the infection, and this information may be examined so that diagnosis and treatment can begin sooner. This is where artificial intelligence meets the diagnostic capabilities of intimate clinicians. X-ray imaging is an effectively available apparatus that can be an astounding option in the COVID-19 diagnosis. The architecture usually used are VGG16, ResNet50, DenseNet121, Xception, ResNet18, etc. This deep learning based COVID detection system can be installed in hospitals for early diagnosis, or it can be used as a second opinion. Keywords: COVID-19, Deep Learning, CNN, CT-Image, Transfer Learning, VGG, ResNet, DenseNet

scholarly journals Deep learning approach for automatic segmentation of ulna and radius in dual-energy X-ray imaging

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Fan Yang ◽  
Xin Weng ◽  
Yuehong Miao ◽  
Yuhui Wu ◽  
Hong Xie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Dual Energy ◽  
Learning Approach ◽  
Mineral Density ◽  
X Ray ◽  
X Ray Imaging ◽  
Residual Block ◽  
Deep Learning Model ◽  
Fold Cross Validation

Abstract Background Segmentation of the ulna and radius is a crucial step for the measurement of bone mineral density (BMD) in dual-energy X-ray imaging in patients suspected of having osteoporosis. Purpose This work aimed to propose a deep learning approach for the accurate automatic segmentation of the ulna and radius in dual-energy X-ray imaging. Methods and materials We developed a deep learning model with residual block (Resblock) for the segmentation of the ulna and radius. Three hundred and sixty subjects were included in the study, and five-fold cross-validation was used to evaluate the performance of the proposed network. The Dice coefficient and Jaccard index were calculated to evaluate the results of segmentation in this study. Results The proposed network model had a better segmentation performance than the previous deep learning-based methods with respect to the automatic segmentation of the ulna and radius. The evaluation results suggested that the average Dice coefficients of the ulna and radius were 0.9835 and 0.9874, with average Jaccard indexes of 0.9680 and 0.9751, respectively. Conclusion The deep learning-based method developed in this study improved the segmentation performance of the ulna and radius in dual-energy X-ray imaging.

scholarly journals DEEP LEARNING (CNN) MODEL FOR COVID-19 DETECTION FROM CHEST XRAY IMAGES

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Ahmed Mohamed ◽  
Ahmed Abdelhady
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Stage ◽  
Open Data ◽  
Disease Outbreak ◽  
Imaging Features ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
Data Source

The Coronavirus disease outbreak result in many people to have severe respira- tory problems and it was recognized as a global health threat. Since the virus is targeting the lungs in the human body initially, chest x-ray imaging features were considered to be useful for the detection of the infection in the early stage. In this study, the chest x-ray data of 130 infected patients from an open data source that referenced Cohen J. Morrison P. Dao L., 2020 was used to build a CNN( Convolutional Neural-Network) model for the early detection of the disease. The model was trained with both infected and not-infected peoples’ chest x-ray images with 100 epochs which led to 0.98 accuracy finally. In order to use this model as a professional diagnosis element, it is highly recommended it be improved with more images and the model can be restructured to get a better accuracy.

scholarly journals Deep learning based high-resolution incoherent x-ray imaging with a single-pixel detector

2020 ◽  
Author(s):  
Yu-Hang He ◽  
Ai-Xin Zhang ◽  
Ming-Fei Li ◽  
Yi-Yi Huang ◽  
Bao-Gang Quan ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Pixel Detector ◽  
X Ray ◽  
X Ray Imaging ◽  
Single Pixel
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