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Related Concept Videos

X-ray Imaging01:24

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 X-rays, and by 1900, X-ray was widely...
Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body being...
Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Related Experiment Video

Updated: May 24, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

YOLO-LXR: An Enhanced Model for Pathology Detection in Chest X-Rays.

Gerasimos Katsagannis1, Barry L Bentley1,2

  • 1Bioengineering Research Group, Cardiff Metropolitan University, United Kingdom.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

This study introduces a novel YOLO-like model for detecting lung pathologies in X-ray images. The enhanced model improves accuracy and efficiency for medical image analysis.

Keywords:
Residual BlockSqueeze-and-Excitation NetworksX-RayYOLO

Related Experiment Videos

Last Updated: May 24, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Object detection algorithms like YOLO excel in balancing speed and accuracy.
  • Their application in medical imaging, especially for X-ray analysis, is an emerging field.
  • Accurate and efficient detection of lung pathologies is crucial for timely diagnosis and treatment.

Purpose of the Study:

  • To propose a YOLO-like object detection model tailored for identifying lung pathologies in X-ray images.
  • To enhance the model's performance by integrating Squeeze-and-Excite and Residual Blocks.
  • To evaluate the model's effectiveness against existing YOLO architectures.

Main Methods:

  • Development of a YOLOv5-based architecture incorporating Squeeze-and-Excite and Residual Blocks.
  • Focus on detecting lung pathologies within chest X-ray datasets.
  • Comparative analysis against various YOLO and YOLO-like models.

Main Results:

  • The proposed model demonstrated superior accuracy compared to most YOLO and YOLO-like architectures.
  • Experiments showed a 3.3% increase in recall and a 1.5% increase in mAP@50 compared to YOLOv5.
  • The architecture achieved significant improvements in detecting lung pathologies.

Conclusions:

  • The developed YOLO-like model offers a cutting-edge solution for accurate and efficient lung pathology detection in X-rays.
  • The integration of specific architectural components enhances detection capabilities.
  • The findings encourage further exploration of advanced deep learning models in medical diagnostics.