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X-ray Imaging

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...

Imaging Studies for Cardiovascular System III: X-Ray

Imaging Studies for Cardiovascular System III: X-Ray

The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...

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Related Experiment Video

Updated: Nov 12, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

COVID-19: Automatic detection from X-ray images by utilizing deep learning methods.

Bhawna Nigam¹, Ayan Nigam², Rahul Jain²

¹Institute of Engineering and Technology, Devi Ahilya University, Indore, India.

Expert Systems with Applications

|March 22, 2021

Summary

This summary is machine-generated.

Deep learning models like EfficientNet can accurately diagnose Coronavirus (COVID-19) using chest X-rays and CT scans. This approach offers a rapid, accessible diagnostic tool, especially where RT-PCR kits are limited.

Keywords:

COVID-19 Coronavirus Deep learning Pandemic

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Area of Science:

Medical Imaging
Artificial Intelligence
Infectious Diseases

Background:

The global emergence of Coronavirus (COVID-19) has created a significant public health crisis.
Traditional diagnostic methods like RT-PCR kits face challenges in availability and turnaround time.
Radiological imaging (X-ray, CT scans) shows characteristic changes in COVID-19 patients.

Purpose of the Study:

To develop and evaluate deep learning models for automated Coronavirus detection using radiological images.
To compare the performance of various deep learning architectures for COVID-19 diagnosis.

Main Methods:

Utilized deep learning architectures including VGG16, DenseNet121, Xception, NASNet, and EfficientNet.
Trained models on chest X-ray and CT scan datasets for multiclass classification (COVID-19 positive, normal, other chest conditions).

Main Results:

EfficientNet achieved the highest accuracy at 93.48%.
DenseNet121 and Xception demonstrated strong performance with accuracies of 89.96% and 88.03%, respectively.
The models effectively differentiated between COVID-19, healthy patients, and other respiratory illnesses.

Conclusions:

Deep learning models show significant potential for rapid and accurate COVID-19 diagnosis from radiological images.
These AI-powered systems can serve as valuable tools for radiologists, improving diagnostic speed and accuracy.
The technology is particularly beneficial for underserved regions lacking advanced diagnostic facilities and expert physicians.