FFCAEs: An efficient feature fusion framework using cascaded autoencoders for the identification of gliomas

Gudigar, A., Raghavendra, U., Rao, T. N., Samanth, J., Rajinikanth, V., Satapathy, S. C., Ciaccio, E. J., Wai Yee, C., & Acharya, U. R. (2022). FFCAEs: An efficient feature fusion framework using cascaded autoencoders for the identification of gliomas. International Journal of Imaging Systems and Technology, 33(2), 483–494. Portico. https://doi.org/10.1002/ima.22820
Authors:
Anjan Gudigar
U. Raghavendra
Tejaswi N. Rao
Jyothi Samanth
Venkatesan Rajinikanth
Suresh Chandra Satapathy
Edward J. Ciaccio
Chan Wai Yee
U. Rajendra Acharya
Affiliated Authors:
Edward J. Ciaccio
Columbia Affiliation:
Author Keywords:
cascaded autoencoders
classification accuracy
computer-aided diagnostic tool
feature fusion framework
glioblastoma (gbm)
low-grade glioma (lgg)
magnetic resonance (mr)
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Publication Type:
Article
Unique ID:
10.1002/ima.22820
DOI:
10.1002/ima.22820
Journal:
International Journal of Imaging Systems and Technology
Publication Date:
Data Source:
Scopus
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