Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently,
researchers have shown an increasing interest in developing computer-aided diagnosis …

Computerized methods provide analyses of skin lesions from dermoscopy images
automatically. However, the images acquired from dermoscopy devices are noisy and cause …

Background: The dermatologist widely uses digital dermoscopy for the detection of
melanoma. The accurate detection of melanoma by clinicians is subjective and further …

Background: State-of-the-art classifiers based on convolutional neural networks (CNNs)
were shown to classify images of skin cancer on par with dermatologists and could enable …

Melanoma is the fastest growing and most lethal cancer among all forms of skin cancer.
Deep learning methods, mainly convolutional neural networks (CNNs) have recently …

The recent emergence of deep learning methods for medical image analysis has enabled
the development of intelligent medical imaging-based diagnosis systems that can assist the …

Skin cancer is one of the most common types of cancer worldwide. Over the past few years,
different approaches have been proposed to deal with automated skin cancer detection …

Abstract Background and Objective Skin lesion segmentation is an important but
challenging task in computer-aided diagnosis of dermoscopy images. Many segmentation …

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 …

Image‐based computer aided diagnosis systems have significant potential for screening
and early detection of malignant melanoma. We review the state of the art in these systems …