Propensity of skin diseases to manifest in a variety of forms, lack and maldistribution of
qualified dermatologists, and exigency of timely and accurate diagnosis call for automated …

Background The emergence of the deep convolutional neural network (CNN) greatly
improves the quality of computer-aided supporting systems. However, due to the challenges …

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

Skin problems are among the most common ailments on Earth. Despite its popularity,
assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles …

Efficient methods developed with deep learning in the last ten years have provided
objectivity and high accuracy in the diagnosis of skin diseases. They also support accurate …

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most
cases are seen instead by general practitioners with lower diagnostic accuracy. We present …

Traditional approaches to automatic diagnosis of skin lesions consisted of classifiers
working on sets of hand-crafted features, some of which modeled lesion aspects of special …

A complex topic such as dermatology makes it one of the most unexpected and challenging
professions to diagnose due to the complexities of the subject matter involved. According to …

Background As the booming of deep learning era, especially the advances in convolutional
neural networks (CNNs), CNNs have been applied in medicine fields like radiology and …

The application of deep learning methods to diagnose diseases has become a new
research topic in the medical field. In the field of medicine, skin disease is one of the most …