eprintid: 15662 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/56/62 datestamp: 2023-11-10 03:30:17 lastmod: 2023-11-10 03:30:17 status_changed: 2023-11-10 02:00:03 type: article metadata_visibility: show creators_name: Sultana, S. creators_name: Hussain, S.S. creators_name: Hashmani, M. creators_name: Ahmad, J. creators_name: Zubair, M. title: A deep learning hybrid ensemble fusion for chest radiograph classification ispublished: pub 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 note: cited By 2 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. date: 2021 publisher: Czech Technical University in Prague official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115448136&doi=10.14311%2fNNW.2021.31.010&partnerID=40&md5=0282577f1681e52f598f23bce3eff081 id_number: 10.14311/NNW.2021.31.010 full_text_status: none publication: Neural Network World volume: 31 number: 3 pagerange: 199-209 refereed: TRUE issn: 12100552 citation: 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