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

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

Background Recent studies have demonstrated the use of convolutional neural networks
(CNNs) to classify images of melanoma with accuracies comparable to those achieved by …

Importance Convolutional neural networks (CNNs) achieve expert-level accuracy in the
diagnosis of pigmented melanocytic lesions. However, the most common types of skin …

Background Incidence of skin cancer is one of the global burdens of malignancies that
increase each year, with melanoma being the deadliest one. Imaging-based automated skin …

Importance A high proportion of suspicious pigmented skin lesions referred for investigation
are benign. Techniques to improve the accuracy of melanoma diagnoses throughout the …

Background Deep learning convolutional neural networks (CNNs) show great potential for
melanoma diagnosis. Melanoma thickness at diagnosis among others depends on …

Background Melanoma is the most dangerous type of skin cancer but is curable if detected
early. Recent publications demonstrated that artificial intelligence is capable in classifying …

Background Recently, convolutional neural networks (CNNs) systematically outperformed
dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a …

Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs)
to classify images of melanoma, with accuracies comparable to those achieved by …