Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Area of Science:

• Dermatology and Medical Imaging
• Artificial Intelligence in Healthcare

Background:

• Skin lesion analysis is crucial for diagnosing dermatological disorders.
• Traditional methods face challenges in accuracy and efficiency.
• Advancements in computer vision and machine learning offer new possibilities.

Purpose of the Study:

• To provide a comprehensive review of recent learning-based methods for skin lesion analysis.
• To examine techniques for skin lesion classification, segmentation, and detection.
• To identify current trends, challenges, and future research directions.

Main Methods:

• Systematic review of state-of-the-art research papers.
• Analysis of deep learning and conventional machine learning techniques.
• Examination of segmentation algorithms (deep-learning-based, graph-based, region-based).

Main Results:

• Detailed review of skin lesion classification methods using various image formats.
• Exploration of segmentation and detection techniques for precise lesion border identification.
• Discussion of key datasets, challenges, and evaluation metrics in the field.

Conclusions:

• Machine learning significantly enhances skin lesion analysis capabilities.
• Accurate classification, segmentation, and detection are vital for improved patient outcomes.
• Further research is needed to address existing challenges and explore future directions.