A deep learning hybrid ensemble fusion for chest radiograph classification

• Main Authors: Sultana, S., Hussain, S.S., Hashmani, M., Ahmad, J., Zubair, M.
• Format: Article
• Published: Czech Technical University in Prague 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115448136&doi=10.14311%2fNNW.2021.31.010&partnerID=40&md5=0282577f1681e52f598f23bce3eff081; http://eprints.utp.edu.my/29425/

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.