Skin Lesion Segmentation with C-UNet.

Area of Science:

• Medical image analysis
• Artificial intelligence in healthcare
• Dermatology

Background:

• Malignant melanoma is a significant global health concern.
• Timely diagnosis and treatment are crucial for improving patient survival rates.
• Accurate segmentation of skin lesions is essential for effective diagnosis.

Purpose of the Study:

• To propose a novel deep learning model, C-UNet, for enhanced skin lesion segmentation.
• To improve the accuracy and robustness of automated skin lesion analysis.
• To provide a tool that aids in the early detection of melanoma.

Main Methods:

• Development of the C-UNet deep learning architecture incorporating Inception-like blocks, recurrent convolutional blocks, and dilated convolutional layers.
• Application of a fine-tuning technique using Dice loss after initial training with cross-entropy loss.
• Post-processing of predicted label maps using conditional random fields for improved smoothness.

Main Results:

• The proposed C-UNet model achieved higher accuracy in skin lesion segmentation compared to the standard UNet.
• C-UNet demonstrated more robust segmentation results, indicating better generalization.
• Experimental validation confirmed the effectiveness of the integrated architectural components and training strategy.

Conclusions:

• The C-UNet model represents a significant advancement in deep learning for skin lesion segmentation.
• This method offers improved accuracy and robustness, potentially aiding clinicians in melanoma diagnosis.
• Further research can explore clinical integration and validation of C-UNet for real-world applications.