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Radiography, Thoracic

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Automatic detection of COVID-19 from chest radiographs using deep learning.

Radiography (London, England : 1995)
INTRODUCTION: The breakdown of a deadly infectious disease caused by a newly discovered coronavirus (named SARS n-CoV2) back in December 2019 has shown no respite to slow or stop in general. This contagious disease has spread across different lengths...

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.

Scientific reports
The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the ...

Deep Mining External Imperfect Data for Chest X-Ray Disease Screening.

IEEE transactions on medical imaging
Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large distribution. Therefore, it is substantial to integrate knowledge from mu...

Lesion-aware convolutional neural network for chest radiograph classification.

Clinical radiology
AIM: To investigate the performance of a deep-learning approach termed lesion-aware convolutional neural network (LACNN) to identify 14 different thoracic diseases on chest X-rays (CXRs).

Reproducibility of abnormality detection on chest radiographs using convolutional neural network in paired radiographs obtained within a short-term interval.

Scientific reports
We evaluated the reproducibility of computer-aided detections (CADs) with a convolutional neural network (CNN) on chest radiographs (CXRs) of abnormal pulmonary patterns in patients, acquired within a short-term interval. Anonymized CXRs (n = 9792) o...

Diagnosis of common pulmonary diseases in children by X-ray images and deep learning.

Scientific reports
Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed ...

Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation.

AJR. American journal of roentgenology
The objective of our study was to assess the effect of the combination of deep learning-based denoising (DLD) and iterative reconstruction (IR) on image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on chest ultra-low-dos...

Extravalidation and reproducibility results of a commercial deep learning-based automatic detection algorithm for pulmonary nodules on chest radiographs at tertiary hospital.

Journal of medical imaging and radiation oncology
INTRODUCTION: To extra validate and evaluate the reproducibility of a commercial deep convolutional neural network (DCNN) algorithm for pulmonary nodules on chest radiographs (CRs) and to compare its performance with radiologists.

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.

Medical image analysis
The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-...

Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence.

Radiology
Background Radiologists are proficient in differentiating between chest radiographs with and without symptoms of pneumonia but have found it more challenging to differentiate coronavirus disease 2019 (COVID-19) pneumonia from non-COVID-19 pneumonia o...