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001-es BibID:	BIBFORM077186
Első szerző:	Pap Melinda (informatikus)
Cím:	Automatic Pigment Network Classification Using a Combination of Classical Texture Descriptors and CNN Features / Melinda Pap, Balázs Harangi, András Hajdu
Dátum:	2017
Megjegyzések:	The presence of atypical (irregular) pigment networks can be a symptom of melanoma malignum in skin lesions, thus, their proper recognition is a critical task. For object classification problems, the application of deep convolutional neural nets (CNN) receives priority attention nowadays for their high recognition rate. The descriptive features found by CNNs are more hidden than the classically applied ones for texture recognition. In this paper, we investigate whether CNN features outperform the classical texture descriptors in the classification of typical/atypical pigment network. Beyond performing this analysis, we have also found that the aggregation of CNN and classical features within a joint classification framework had a superior performance. Specifically, the union of the CNN and classical feature sets leads to a much higher stability in classification performance for various classifiers. As for quantitative figures, we have reached 90.44% recognition accuracy using a specific subset of this combined feature set obtained by linear forward feature selection and using a Bayes Net as classifier.
ISBN:	978-1-5386-1710-6
Tárgyszavak:	Műszaki tudományok Informatikai tudományok könyvfejezet pigment network machine learning feature extraction deep learning
Megjelenés:	Proceedings 2017 IEEE 30th International Symposium on Computer-Based Medical Systems CBMS 2017 / Panagiotis D. Bamidis, Stathis Th. Konstantinidis, Pedro Pereira Rodrigues. - p. 343-348. -
További szerzők:	Harangi Balázs (1986-) (programtervező matematikus) Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:	OTKA-115479 OTKA GINOP-2.1.1-15-2015-00376 GINOP
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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