Objective: This paper addresses two key problems of skin lesion classification. The first
problem is the effective use of high-resolution images with pretrained standard architectures …

As one of the most common cancers globally, the incidence of skin cancer has been rising.
Dermoscopy-based classification has become the most effective method for the diagnosis of …

Knowing when a machine learning system is not confident about its prediction is crucial in
medical domains where safety is critical. Ideally, a machine learning algorithm should make …

Background and objectives: The number of deaths worldwide due to melanoma has risen in
recent times, in part because melanoma is the most aggressive type of skin cancer …

The success of deep learning in skin lesion classification mainly depends on the ultra-deep
neural network and the significantly large training data set. Deep learning training is usually …

In this work, we focus in the analysis of dermoscopy images using convolutional neural
networks (CNNs). More specifically, we investigate the value of augmenting CNN inputs with …

Melanoma skin cancer is one of the fastest growing and deadliest cancers in the world.
Therefore, the classification of melanoma in dermoscopy images is of great significance for …

Automatic intelligent skin lesion recognition is crucial for elevating detection accuracy,
enhancing diagnostic efficiency, and mitigating the risk of melanoma mortality. Despite …

Multimodal data combined in an integrated dataset can be used to aim the identification of
instrumental biological actions that trigger the development of a disease. In this paper, we …

In this work, we report the use of convolutional neural networks for the detection of malignant
melanomas against nevus skin lesions in a dataset of dermoscopic images of the same …