Background Evolving dermoscopic terminology motivated us to initiate a new consensus.
Objective We sought to establish a dictionary of standardized terms. Methods We reviewed …

Background Melanoma has one of the fastest rising incidence rates of any cancer. It
accounts for a small percentage of skin cancer cases but is responsible for the majority of …

Prior skin image datasets have not addressed patient-level information obtained from
multiple skin lesions from the same patient. Though artificial intelligence classification …

The variation in skin textures and injuries, as well as the detection and classification of skin
cancer, is a difficult task. Manually detecting skin lesions from dermoscopy images is a …

Background Deep learning convolutional neural networks (CNN) may facilitate melanoma
detection, but data comparing a CNN's diagnostic performance to larger groups of …

In this article, we describe the design and implementation of a publicly accessible
dermatology image analysis benchmark challenge. The goal of the challenge is to sup-port …

Importance Deep learning convolutional neural networks (CNNs) have shown a
performance at the level of dermatologists in the diagnosis of melanoma. Accordingly …

As per recent developments in medical science, the skin cancer is considered as one of the
common type disease in human body. Although the presence of melanoma is viewed as a …

The complex detection background and lesion features make the automatic detection of
dermoscopy image lesions face many challenges. The previous solutions mainly focus on …

In recent days, skin cancer is seen as one of the most Hazardous form of the Cancers found
in Humans. Skin cancer is found in various types such as Melanoma, Basal and Squamous …