A review on deep learning in medical image analysis

Area of Science:

• Artificial Intelligence in Medicine
• Deep Learning for Medical Imaging
• Computational Pathology

Background:

• Artificial intelligence (AI), especially deep learning (DL), is rapidly advancing.
• DL techniques are increasingly applied in diverse medical fields.
• Medical image analysis benefits significantly from DL for pattern recognition.

Purpose of the Study:

• To present fundamental information and state-of-the-art deep learning approaches in medical image processing.
• To outline research on medical image processing using deep learning.
• To define and implement key guidelines for deep learning in medical imaging analysis.

Main Methods:

• Review of existing studies applying deep learning to various medical imaging regions.
• Exploration of deep learning networks for big data analysis, knowledge exploration, and prediction.
• Presentation of fundamental and advanced deep learning methods for medical image analysis.

Main Results:

• Deep learning effectively identifies, classifies, and quantifies patterns in clinical images.
• Successful application of DL across neuroimaging, pathology, cardiology, and musculoskeletal imaging.
• DL networks demonstrate capability for information exploration and knowledge-based prediction in big data.

Conclusions:

• Deep learning offers powerful tools for enhancing medical image processing and analysis.
• The paper provides a foundational understanding and practical guidelines for DL implementation in medical imaging.
• Continued advancements in DL promise significant future contributions to diagnostic accuracy and patient care.