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.
Dermatological Classification and Segmentation by Means of Convolutional Neural Network / Grignaffini, F.; Barbuto, F.; Troiano, M.; Piazzo, L.; Mangini, F.; Cantisani, C.; Schiavella, G.; Manti, D.; Ferranti, R.; Frezza, F.. - (2025). ( Maker Faire Rome 2025 Roma ).
Dermatological Classification and Segmentation by Means of Convolutional Neural Network
F. Grignaffini;F. Barbuto;M. Troiano;L. Piazzo;F. Mangini;C. Cantisani;G. Schiavella;R. Ferranti;F. Frezza
2025
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.File allegati a questo prodotto
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