WIELOKLASOWA KLASYFI KACJA Z NAM ION SK Ó RNYCH W OPARCIU O GŁĘBOKIE SIECI NEURONOW E
Magdalena Michalska
mmagamichalska@gmail.comPolitechnika Lubelska, Katedra Elektroniki i Technik Informacyjnych (Polska)
http://orcid.org/0000-0002-0874-3285
Abstrakt
Choroby skóry diagnozowane za pomocą dermatoskopii są coraz powszechniejsze. Wykorzytanie skomputeryzowanych systemów diagnostyki staje się niezwykle skuteczne. Nieiwazyjne metody diagnostyki, jakimi są głębokie sieci neuronowe są coraz powszechniejszym narzędziem badanym przez naukowców. W artykule przedstawiono przegląd wybranych głównych zagadnień związanych w procesem klasyfikacji wieloklasowej: etap wyboru bazy danych, wstępnego przetwarzania obrazów, doboru zestawu danych uczących, narzędzi klasyfikacji, etapu trenowania sieci i otrzymania wyników końcowych. Opisane działania zostały zaimplementowane za pomocą dostępnych głębokich sieci neuronowych. W artykule zwrócono uwagę na wyniki końcowe dostępnych modeli, takich jak skuteczność, specyficzność, dokładność klasyfikacji dla różnej ilości klas i dostępnych zestawów danych.
Słowa kluczowe:
obrazy dermatoskopowe, wieloklasowa klasyfikacja, zmiany skórne, głębokie sieci neuronoweBibliografia
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Autorzy
Magdalena Michalskammagamichalska@gmail.com
Politechnika Lubelska, Katedra Elektroniki i Technik Informacyjnych Polska
http://orcid.org/0000-0002-0874-3285
Statystyki
Abstract views: 197PDF downloads: 177
Licencja
Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa – Na tych samych warunkach 4.0 Miedzynarodowe.