Convolutional neural networks for the automatic diagnosis of melanoma: An extensive experimental study

Key Points

• Automated diagnosis of melanoma achieved high accuracy, with a precision rate of over 90%.
• The analysis utilized a dataset of thousands of labeled skin images for training and validation.
• Implementing convolutional neural networks enabled efficient image classification and feature extraction.
• Highlighting the potential for AI-based tools in dermatology to enhance diagnosis accuracy and minimize human error.