Comprehensive Summary
This article presents EASNet, an edge-aware deep learning network developed to improve skin lesion segmentation, a critical step in computer-aided dermatological diagnosis. The model addresses common challenges in lesion segmentation, such as irregular boundaries, low contrast, and visual noise, by integrating boundary-aware attention mechanisms with frequency-domain information extracted using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). By combining spatial and frequency features, EASNet enhances its ability to distinguish lesion borders from surrounding skin. When evaluated on benchmark datasets, the model demonstrated improved segmentation accuracy and boundary precision compared to several existing convolutional neural network approaches, highlighting its effectiveness in capturing fine-grained lesion details.
Outcomes and Implications
The findings of this study suggest that incorporating edge-aware and frequency-based attention mechanisms can significantly improve automated skin lesion segmentation, which has important implications for early skin cancer detection. More accurate segmentation can enhance downstream tasks such as lesion classification and malignancy risk assessment, potentially supporting clinicians in making faster and more reliable diagnostic decisions. Additionally, EASNet’s improved boundary detection may reduce false positives and unnecessary biopsies, helping to optimize clinical workflows. However, further validation across diverse patient populations and real-world clinical settings is necessary to ensure generalizability and equity in performance, particularly for individuals with varied skin tones. Overall, this research contributes to the growing body of work demonstrating how advanced AI architectures can meaningfully support dermatological care.