Skin Cancer Detection Using Deep Learning-A Review.

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Skin cancer is a significant global health concern and a leading cause of cancer-related deaths worldwide.
• Current diagnostic methods, primarily visual inspection, suffer from limitations in accuracy, underscoring the need for improved detection strategies.
• Early diagnosis is critical for reducing mortality rates associated with skin cancer.

Purpose of the Study:

• To survey recent advancements in deep learning methodologies for skin cancer classification.
• To provide an overview of commonly utilized deep learning models in dermatological research.
• To identify and discuss prevalent datasets employed in the training and validation of skin cancer classification algorithms.

Main Methods:

• Systematic review of recent research articles focusing on deep learning for skin cancer classification.
• Analysis of commonly used deep learning architectures (e.g., Convolutional Neural Networks).
• Examination of publicly available and proprietary datasets used in skin cancer image analysis.

Main Results:

• Deep learning models demonstrate significant potential in enhancing the accuracy of skin cancer diagnosis compared to traditional methods.
• Convolutional Neural Networks are frequently employed and show high performance in classifying various skin lesion types.
• The availability and quality of diverse datasets are crucial for the generalization and robustness of deep learning models.

Conclusions:

• Deep learning offers a promising avenue for improving early and accurate skin cancer diagnosis.
• Further research and development in deep learning models and curated datasets are essential for clinical translation.
• AI-assisted diagnostic tools can support dermatologists, potentially leading to better patient outcomes and reduced healthcare costs.