The integration of artificial intelligence in healthcare, particularly in radiology, is shifting paradigms in diagnostics.

We have started building our proposed model by collecting the dataset from six different data sources and named it CovRecker. It contains both X-ray and CT scan images (i.e., chest radiology images).

The following is a summary of “Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks,” ...

As cold and flu season rolls around, respiratory illnesses are often top of mind for many of us. Recently, terms like “white ...

Scintillators are detectors that make high-energy X-rays or particles visible through flashes of light to form an image.

This study focuses on utilizing DL to distinguish between chest X-ray images associated with pneumonia, COVID-19, and normal cases. In order to classify these chest X-ray images and return the ...

China: In a recent study published in Orthopaedic Surgery, researchers discovered that chest CT scans—commonly used for ...

The high-tech, programmable mannequins in the new lab at the Cumberland County medical center can simulate lung sounds, ...

The "Radiology Market by Modality (Computed Tomography, Fluoroscopy, Magnetic Resonance Imaging), Application (Cardiology, ...

Therefore, we propose an intra- and inter-correlation learning framework for chest X-ray anomaly detection. Firstly, to better leverage the similar anatomical structure information in chest X-rays, we ...