relation: https://khub.utp.edu.my/scholars/15662/
title: A deep learning hybrid ensemble fusion for chest radiograph classification
creator: Sultana, S.
creator: Hussain, S.S.
creator: Hashmani, M.
creator: Ahmad, J.
creator: Zubair, M.
description: Biomedical imaging, archiving, and classification is the recent challenge of computer-aided medical imaging. The popular and influential Deep Learning methods predict and congregate distinct markable features of ambiguity in radiographs precisely and accurately. This study submits a new topology of a deep learning network for chest radiograph classification. In this approach, a hybrid ensemble fusion of neural network topology can better diagnose ambiguities with high precision. The proposed topology also compares statistical findings with three optimizers and the most possible varying essential attributes of dropout probabilities and learning rates. The performance as a function of the AUCROC of this model is measured on the Chest Xpert dataset. © CTU FTS 2021.
publisher: Czech Technical University in Prague
date: 2021
type: Article
type: PeerReviewed
identifier:   Sultana, S. and Hussain, S.S. and Hashmani, M. and Ahmad, J. and Zubair, M.  (2021) A deep learning hybrid ensemble fusion for chest radiograph classification.  Neural Network World, 31 (3).  pp. 199-209.  ISSN 12100552     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115448136&doi=10.14311%2fNNW.2021.31.010&partnerID=40&md5=0282577f1681e52f598f23bce3eff081
relation: 10.14311/NNW.2021.31.010
identifier: 10.14311/NNW.2021.31.010