The aim of the present work is to try to understand whether, using the original (unprocessed) dermoscopic images and the additional texture information from the whole (unsegmented) images, a Convolutional Neural Network (CNN) can effectively distinguish between nevi and melanomas, which have similar features and are therefore difficult to diagnose.
Dermoscopic Image Preprocessing and Classification Using Convolutional Neural Network / Grignaffini, F.; Barbuto, F.; Troiano, M.; Piazzo, L.; Mangini, F.; Cantisani, C.; De Martinis, G.; Sbriccoli, L.; Frezza, F.. - (2024). (Intervento presentato al convegno Maker Faire Rome 2024 tenutosi a Roma).
Dermoscopic Image Preprocessing and Classification Using Convolutional Neural Network
F. Grignaffini;F. Barbuto;M. Troiano;L. Piazzo;F. Mangini;C. Cantisani;G. De Martinis;L. Sbriccoli;F. Frezza
2024
Abstract
The aim of the present work is to try to understand whether, using the original (unprocessed) dermoscopic images and the additional texture information from the whole (unsegmented) images, a Convolutional Neural Network (CNN) can effectively distinguish between nevi and melanomas, which have similar features and are therefore difficult to diagnose.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
