Background: Chest X-ray (CXR) imaging is the most common examination; however, no
automatic quality assurance (QA) system using deep learning (DL) has been established for …

Although the widespread use of digital imaging has enabled real-time image display,
images in chest X-ray examinations can be confirmed by the radiologist's eyes. Considering …

Purpose To automate diagnostic chest radiograph imaging quality control (lung inclusion at
all four edges, patient rotation, and correct inspiration) using convolutional neural network …

Objective: To explore the application value of convolution neural network in quality control
(QC) of chest digital radiology (DR) images. Methods: We classified and labeled 1618 chest …

Background Digital radiography is the most commonly utilized medical imaging technique
worldwide, and the quality of radiographs plays a crucial role in accurate disease diagnosis …

The quality of chest radiographs is a practical issue because deviations from quality
standards cost radiologists' time, may lead to misdiagnosis and hold legal risks. Automatic …

Purpose To standardize the radiography imaging procedure, an image quality control
framework using the deep learning technique was developed to segment and evaluate …

Objectives Develop and evaluate the performance of deep learning and linear regression
cascade algorithms for automated assessment of the image layout and position of chest …

The purpose of this study was learning about chest X-ray image classification and accuracy
research through Deep Learning using big data technology with Convolution Neural …

Background Chest computed tomography (CT) image quality impacts radiologists'
diagnoses. Pre-diagnostic image quality assessment is essential but labor-intensive and …