Advances in Deep Learning-Based Medical Image Analysis.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Deep Learning

Background:

• Artificial intelligence (AI) and deep learning are rapidly advancing medical image analysis.
• Convolutional neural networks (CNNs) are key deep learning techniques applied in this field.
• Research spans multiple clinical applications across major human body systems.

Purpose of the Study:

• To review recent progress in deep learning for medical image analysis.
• To discuss current challenges and propose future research directions.
• To highlight state-of-the-art clinical applications of deep learning in medicine.

Main Methods:

• Literature review of deep learning advancements in medical image analysis.
• Focus on convolutional neural network (CNN)-based techniques.
• Analysis of applications in nervous, cardiovascular, digestive, and skeletal systems.

Main Results:

• Deep learning models demonstrate high accuracy, efficiency, stability, and scalability in medical image analysis.
• Successful applications identified across four major human body systems.
• Small-scale medical datasets pose a significant limitation to clinical applicability.

Conclusions:

• Deep learning technologies have achieved significant success in medical image analysis.
• Addressing the need for large, high-quality datasets is crucial for clinical integration.
• Future directions include federated learning, benchmark dataset creation, and incorporating domain knowledge.