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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...

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Related Experiment Video

Updated: Sep 13, 2025

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

Automatic Fracture Detection Convolutional Neural Network with Multiple Attention Blocks Using Multi-Region X-Ray

Rashadul Islam Sumon¹, Mejbah Ahammad², Md Ariful Islam Mozumder¹

¹Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae-si 50834, Republic of Korea.

Life (Basel, Switzerland)

|July 29, 2025

Summary

This summary is machine-generated.

This study introduces an advanced artificial intelligence (AI) model using attention-based deep learning for improved fracture detection in X-ray images. The AI model significantly enhances diagnostic accuracy, aiding timely medical treatment and better patient outcomes.

Keywords:

CBMA CNN attention module fracture multi region

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Last Updated: Sep 13, 2025

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Semiautomated Longitudinal Microcomputed Tomography-based Quantitative Structural Analysis of a Nude Rat Osteoporosis-related Vertebral Fracture Model

Published on: September 28, 2017

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Accurate fracture detection in X-rays is crucial for timely medical intervention.
Current methods can be time-consuming and may have limitations in accuracy.
Deep learning offers potential for automated and enhanced diagnostic capabilities.

Purpose of the Study:

To develop and evaluate an advanced combined attention Convolutional Neural Network (CNN) model for improved fracture detection in X-ray images.
To assess the diagnostic efficacy of the AI model before and after optimization.
To demonstrate the role of attention mechanisms in enhancing feature representation for medical image analysis.

Main Methods:

Development of a combined attention CNN model incorporating squeeze blocks and convolutional block attention module (CBAM).
Training and evaluation using a diverse dataset of fractured and non-fractured X-rays from multiple anatomical locations (hips, knees, lumbar, limbs).
Assessment of diagnostic performance using computed tomography X-ray images, comparing pre- and post-optimization efficacy.

Main Results:

The AI model achieved a high training accuracy of 99.98% and a validation accuracy of 96.72%.
The attention-based CNN effectively focused on relevant features, improving fracture detection capabilities.
The model demonstrated strong generalization across various anatomical locations.

Conclusions:

The developed attention-based CNN model shows significant promise for accurate and automated fracture detection in medical imaging.
Incorporating attention mechanisms enhances the model's ability to interpret complex features in X-rays.
This AI approach can support clinicians, reduce examination time, and improve patient outcomes.