Medical image analysis using deep learning algorithms.

Area of Science:

• Medical Image Analysis
• Artificial Intelligence in Healthcare
• Deep Learning Applications

Background:

• Deep learning (DL) offers significant potential for enhancing medical image analysis in healthcare.
• Existing DL techniques face challenges in real-time analysis of complex medical datasets.
• This review addresses the need for advanced DL approaches in medical image analysis.

Approach:

• Systematic categorization of state-of-the-art DL techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Long Short-term Memory (LSTM) models.
• Assessment of techniques based on principles, advantages, limitations, methodologies, simulation environments, and datasets.
• Analysis of evaluation metrics such as accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.

Key Points:

• Python is the predominant programming language used in recent DL research for medical imaging.
• The majority of reviewed studies were published in 2021, indicating a rapidly evolving field.
• Identified challenges hinder the widespread implementation of DL in medical image analysis.

Conclusions:

• This review provides a comprehensive overview of current DL advancements and practical applications in medical image analysis.
• Insights gained will guide future research toward progressive advancements in medical healthcare image analysis.
• Further studies are needed to overcome implementation barriers and fully leverage DL's potential in healthcare.