Találati lista | Tudóstér

001-es BibID:	BIBFORM077188
Első szerző:	Harangi Balázs (programtervező matematikus)
Cím:	Classification of skin lesions using an ensemble of deep neural networks / Balazs Harangi, Agnes Baran, Andras Hajdu
Dátum:	2018
Megjegyzések:	Skin cancer is among the deadliest variants of cancer if not recognized and treated in time. This work focuses on the identification of this disease using an ensemble of state-of-the-art deep learning approaches. More specifically, we propose the aggregation of robust convolutional neural networks (CNNs) into one neural net architecture, where the final classification is achieved based on the weighted output of the member CNNs. Since our framework is realized within a single neural net architecture, all the parameters of the member CNNs and the weights applied in the fusion can be determined by backpropagation routinely applied for such tasks. The presented ensemble consists of the CNNs AlexNet, VGGNet, GoogLeNet, all of which have been won in subsequent years the most prominent worldwide image classification challenge ImageNet. For an objective evaluation of our approach, we have tested its performance on the official test database of the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 challenge on Skin Lesion Analysis Towards Melanoma Detection dedicated to skin cancer recognition. Our experimental studies show that the proposed approach is competitive in this field. Moreover, the ensemble-based approach outperformed all of its member CNNs.
ISBN:	978-1-5386-3646-6
Tárgyszavak:	Műszaki tudományok Informatikai tudományok könyvfejezet
Megjelenés:	2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) / Gregg Suaning, Olaf Dossel. - p. 2575-2578. -
További szerzők:	Baran Ágnes (1972-) (matematikus) Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:	EFOP-3.6.2-16-2017-00015 EFOP GINOP-2.1.1-15-2015-00376 GINOP
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: