Skin Lesion Segmentation Using U-Net for Automated Dermatological Image Analysis

AI in dermatology has made a huge impact in the detection of skin cancer. One of the key advancements is the use of the U-Net architecture for skin lesion segmentation. U-Net as an encoder-decoder network learns low- and high-level features efficiently, ideally appropriate for biomedical image segmentation. The trained U-Net network on the ISIC 2018 dataset resulted in a Dice Similarity Coefficient of 0.89, Jaccard Index of 0.82, and the sensitivity of 91.5%, which reflects its higher ability to detect lesion boundaries. AI models are effective at classification with 92.3% accuracy, 89.7% precision, and 90.1% F1-score in benign vs. malignant lesion detection. Segmentation along with classification decreases the time for diagnosis with less human error. AI diagnostic accuracy can be equal to or even better than dermatologists in some cases. The combination of U-Net segmentation with AI classification is a critical step forward in the automatic analysis of dermatological images. With near-expert performance from these models, patient outcomes can be enhanced by early and accurate detection of skin cancer. Future efforts will be aimed at additional model generalization and clinical deployment of AI diagnostic systems.