Applications of Neural Networks in Biomedical Data Analysis

Area of Science:

• Biomedical Data Analysis
• Artificial Intelligence in Medicine
• Bioimage Informatics

Background:

• Artificial neural networks (ANNs), or neural networks, are crucial for deep learning.
• Past hardware limitations restricted ANNs; advancements since the early 2000s have enabled complex network training.
• Deep learning is now integral to biomedicine, spanning image analysis, diagnostics, and forensics.

Purpose of the Study:

• To review the latest advancements in neural networks for deep learning applications.
• To focus on the application of these networks in analyzing biomedical data, especially biomarkers within bioimages.
• To provide a quantitative analysis of publication trends regarding neural network types and their usage across scientific fields.

Main Methods:

• Review of current neural network architectures and their functionalities.
• In-depth examination of technical aspects, including activation functions and deep learning frameworks.
• Bibliometric analysis of scientific publications to track the adoption of different neural network types over time.

Main Results:

• Discussion of state-of-the-art neural networks applicable to biomedical data.
• Highlighting the importance of ANNs in extracting biomarkers from bioimage data.
• Quantitative insights into the evolution of neural network usage in scientific publications, showing increasing trends.

Conclusions:

• Neural networks are powerful tools transforming biomedical research and diagnostics.
• The review provides a comprehensive overview of network types and technical considerations for biomedical applications.
• Publication data confirms the growing significance and diverse application of deep learning in science.