Medical image analysis with artificial neural networks

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer Vision

Background:

• Neural networks are increasingly prevalent in medical imaging research.
• Existing literature covers various applications, necessitating a focused survey.

Purpose of the Study:

• To provide a focused literature survey on recent neural network developments in medical imaging.
• To increase awareness of neural network applications in computer-aided diagnosis, image segmentation, edge detection, and image registration.
• To establish a foundation for further research and practical development in the field.

Main Methods:

• Literature survey of recent neural network developments in medical imaging.
• Detailed explanation of representative techniques and algorithms.
• Analysis of neural network applications in computer-aided diagnosis, medical image segmentation, edge detection, and registration.
• Comparison of various neural network applications in medical imaging.

Main Results:

• Identified key neural network applications in medical image analysis, including diagnosis, segmentation, and registration.
• Provided detailed examples of how fixed neural networks can solve medical imaging problems.
• Illustrated how medical images can be analyzed, processed, and characterized using neural networks.
• Explored potential expansions of neural networks for future medical imaging challenges.

Conclusions:

• Neural networks offer significant potential for advancing computer-aided diagnosis and medical image analysis.
• The survey provides a global view of computational intelligence applications in medical imaging.
• Further research and practical development are encouraged based on the presented findings.