A deep learning hybrid ensemble fusion for chest radiograph classification

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.

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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.

Item Type: Article
Impact Factor: cited By 0
Uncontrolled Keywords: Classification (of information); Computer aided diagnosis; Deep learning; Radiography; Topology, 2d image dataset; 2D images; Adam; Chest radiographs; Dropout; Image datasets; Learning rates; Neoteric neural network model; Neural network model; Rmsprop; SGDM, Medical imaging
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 01:52
Last Modified: 25 Mar 2022 01:52
URI: http://scholars.utp.edu.my/id/eprint/29425

Actions (login required)

View Item
View Item