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X-Rays

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CNN-RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images.

Sensors (Basel, Switzerland)
The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both ...

Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays.

Scientific reports
Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians' decision-making is underexplored. In this study, physicians received X-rays with correct diagno...

COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods.

International journal of environmental research and public health
Since December 2019, the coronavirus disease has significantly affected millions of people. Given the effect this disease has on the pulmonary systems of humans, there is a need for chest radiographic imaging (CXR) for monitoring the disease and prev...

Deep learning classification of active tuberculosis lung zones wise manifestations using chest X-rays: a multi label approach.

Scientific reports
Chest X-rays are the most economically viable diagnostic imaging test for active pulmonary tuberculosis screening despite the high sensitivity and low specificity when interpreted by clinicians or radiologists. Computer aided detection (CAD) algorith...

X-ray energy spectrum estimation based on a virtual computed tomography system.

Biomedical physics & engineering express
This paper presents a method for estimating the x-ray energy spectrum for computed tomography (CT) in the diagnostic energy range from the reconstructed CT image itself. To this end, a virtual CT system was developed, and datasets, including CT image...

A deep learning based dual encoder-decoder framework for anatomical structure segmentation in chest X-ray images.

Scientific reports
Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct str...

Centralized contrastive loss with weakly supervised progressive feature extraction for fine-grained common thorax disease retrieval in chest x-ray.

Medical physics
BACKGROUND: Medical images have already become an essential tool for the diagnosis of many diseases. Thus a large number of medical images are being generated due to the daily routine inspection. An efficient image-based disease retrieval system will...

Deep learning for x-ray scatter correction in dedicated breast CT.

Medical physics
BACKGROUND: Accurate correction of x-ray scatter in dedicated breast computed tomography (bCT) imaging may result in improved visual interpretation and is crucial to achieve quantitative accuracy during image reconstruction and analysis.

A fully automatic target detection and quantification strategy based on object detection convolutional neural network YOLOv3 for one-step X-ray image grading.

Analytical methods : advancing methods and applications
Methods for automatic image analysis are demanded for dealing with the explosively increased imaging data in clinics. Osteoarthritis (OA) is a typical disease diagnosed based on X-ray imaging. Herein, we propose a novel modeling strategy based on YOL...

A deep learning model to identify the fluid overload status in critically ill patients based on chest X-ray images.

Polish archives of internal medicine
INTRODUCTION: Recent studies have highlighted adverse outcomes of fluid overload in critically ill patients. Therefore, its early recognition is essential for the management of these patients.