Optimizing Melanoma Prognosis Through Synergistic Preprocessing and Deep Learning Architecture for Dermoscopic Thickness Prediction
| dc.contributor.author | Nikolaos Ntampakis | |
| dc.contributor.author | Konstantinos Diamantaras | |
| dc.contributor.author | Konstantinos Goulianas | |
| dc.contributor.author | Ioanna Chouvarda | |
| dc.contributor.author | Vasileios Argyriou | |
| dc.contributor.author | Panagiotis Sarigiannidis | |
| dc.date.accessioned | 2024-08-09T17:26:42Z | |
| dc.date.available | 2024-08-09T17:26:42Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | http://orcid.org/0000-0001-6042-0355 | en |
| dc.identifier.uri | https://dspace.iee.ihu.gr/handle/123456789/12366 | |
| dc.publisher | Springer Nature Switzerland | en |
| dc.title | Optimizing Melanoma Prognosis Through Synergistic Preprocessing and Deep Learning Architecture for Dermoscopic Thickness Prediction | en |
| dc.title.alternative | Unknown | en |
| dc.type | book-chapter | en |
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| relation.isAuthorOfPublication | 610adfe0-9ce5-489d-b054-513a670bc5cc | |
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