A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi

•The diagnosis of Atypical melanocytic skin lesions (aMSL) is worldwide challenging.•Deep convolutional neural network (DCNN) provide high accuracy in image recognition.•We developed the first hybrid DCNN model trained with aMSL images and clinical data (maximum diameter, site, age, sex).•Dermoscopi...

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Vydané v:	Journal of dermatological science Ročník 101; číslo 2; s. 115 - 122
Hlavní autori:	Tognetti, Linda, Bonechi, Simone, Andreini, Paolo, Bianchini, Monica, Scarselli, Franco, Cevenini, Gabriele, Moscarella, Elvira, Farnetani, Francesca, Longo, Caterina, Lallas, Aimilios, Carrera, Cristina, Puig, Susana, Tiodorovic, Danica, Perrot, Jean Luc, Pellacani, Giovanni, Argenziano, Giuseppe, Cinotti, Elisa, Cataldo, Gennaro, Balistreri, Alberto, Mecocci, Alessandro, Gori, Marco, Rubegni, Pietro, Cartocci, Alessandra
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 01.02.2021
Predmet:	Cutaneous melanoma Deep convolutional neural network Deep learning Dermoscopy Integrated diagnosis Non-invasive imaging DCNN_aMSL ST Cutaneous melanoma ROC aMSL EM DCNNs Dermoscopy Non-invasive imaging AN AUC CNNs Deep learning SE Deep convolutional neural network SP iDCNN_aMSL Integrated diagnosis
ISSN:	0923-1811, 1873-569X, 1873-569X
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