TLR-Net :Transfer Learning in Residual U-Net for Enhancing Skin Lesion Segmentation

Skin lesion semantic segmentation is a critical task in dermatology, aiding early diagnosis and treatment of skin disorders, including melanoma and other forms of skin cancer. Challenge datasets in skin lesion segmentation play a pivotal role in advancing the field by providing standardised benchmarks, promoting collaboration, and facilitating the development of accurate and clinically relevant segmentation algorithms. This paper presents a novel approach to skin lesion segmentation, focusing on the development of a pretrained model for skin lesion segmentation, leveraging a challenging dataset. Transfer Learning in Residual U-Net (TLR-Net) is proposed in this paper to segment the skin lesions from dermoscopic images. It combines the power of transfer learning and the residual learning framework to achieve highly accurate and efficient skin lesion semantic segmentation. The TLR-Net leverages the U-Net's encoder-decoder architecture with skip connections for effective feature extraction and upsampling. Additionally, it incorporates residual blocks within the network to enable the learning of residual mappings, enabling deeper and more efficient feature extraction. Crucially, transfer learning is employed to initialise the model with pre-trained weights from a large-scale dataset, enhancing its ability to generalise skin lesion semantic segmentation tasks with limited labelled data. We evaluated the TLR-Net on a diverse and challenging skin lesion dataset, demonstrating its superior performance compared to traditional U-Net and other state-of-the-art segmentation architectures. Our results indicate that the TLR-Net provides more precise delineation of skin lesions, computationally efficient and suitable for real-world applications. This advancement has significant implications in dermatological practice, empowering clinicians with a reliable tool for early diagnosis and better patient outcomes.