U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

Abstract The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion
analysis is an emerging field of research that has the potential to alleviate the burden and …

Encoder-decoder networks are state-of-the-art approaches to biomedical image
segmentation, but have two problems: ie, the widely used pooling operations may discard …

Abstract In recent years Deep Learning has brought about a breakthrough in Medical Image
Segmentation. In this regard, U-Net has been the most popular architecture in the medical …

Background and objective Automatic segmentation of skin lesions in dermoscopy images is
still a challenging task due to the large shape variations and indistinct boundaries of the …

Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin
cancer by computerized systems. However, automatic segmentation of skin lesions in …

Medical image segmentation enables doctors to observe lesion regions better and make
accurate diagnostic decisions. Single-branch models such as U-Net have achieved great …

Due to its increasing incidence, skin cancer, and especially melanoma, is a serious health
disease today. The high mortality rate associated with melanoma makes it necessary to …

Skin cancer is a senior public health issue that could profit from computer-aided diagnosis to
decrease the encumbrance of this widespread disease. Researchers have been more …

Skin cancer is one of the most threatening diseases worldwide. However, diagnosing skin
cancer correctly is challenging. Recently, deep learning algorithms have emerged to …