Diagnosis in dermatology is largely based on contextual factors going far beyond the visual and dermoscopic inspection of a lesion. Diagnostic tools such as the different types of dermoscopy, confocal microscopy and optical coherence tomography (OCT) are available and all of these have shown their importance in improving the dermatologist's ability, especially in the diagnosis of skin cancer. Their use, however, remains limited and time consuming, and optimizing their practice appears to be difficult, requiring extensive pre-processing, lesion segmentation and extraction of domain-specific visual features before classification. Over the last two decades, image recognition has been a matter of interest in a large part of our society and in industry, leading to the development of several techniques such as convolutional processing combined with artificial intelligence or neural networks (CNN/ANN). The aim of the present manuscript is to provide a short overview of the most recent data about CNN in the field of dermatology, mainly in skin cancer detection and its diagnosis.

Computational neural network in melanocytic lesions diagnosis: artificial intelligence to improve diagnosis in dermatology? / Aractingi, S.; Pellacani, G.. - In: EUROPEAN JOURNAL OF DERMATOLOGY. - ISSN 1167-1122. - 29:1(2019), pp. 4-7.

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