eprintid: 14683
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/46/83
datestamp: 2023-11-10 03:29:16
lastmod: 2023-11-10 03:29:16
status_changed: 2023-11-10 01:57:32
type: conference_item
metadata_visibility: show
creators_name: Sultana, S.
creators_name: Hussain, S.S.
creators_name: Hashmani, M.
creators_name: Fayez, F.A.
creators_name: Umair, M.
title: A Neoteric Variant of Deep Learning Network for Chest Radiograph Automated Annotation
ispublished: pub
keywords: Automation; Learning systems; Radiography, Biomedical technologies; Chest radiographs; Comparative studies; Learning network; Machine learning models; Visual attributes, Deep learning
note: cited By 1; Conference of 6th International Conference on Computer and Information Sciences, ICCOINS 2021 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:170762
abstract: Automated annotation and classification of chest radiographs is the pressing need for modern biomedical technologies. This is mainly because of the massive volume of radiograph archives. The variants of machine learning models have handled this issue of automated disease annotation. However, the performance is found to be constrained due to the visual attribute dependency. Here, deep learning has come into the focus to submit the contribution for effective and efficient automated disease annotation. In this paper, a new variant of a deep learning network (DLN) is presented for automated annotation. Moreover, the exhaustive parametric comparison of the variant with the classical network and the pre-trained network is presented. The Chest X pert dataset is considered for this comparative study. The simulation results advocated for the effectiveness of devised variants. © 2021 IEEE.
date: 2021
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112476091&doi=10.1109%2fICCOINS49721.2021.9497215&partnerID=40&md5=8b3b9ddde9abeea961375d43791b9723
id_number: 10.1109/ICCOINS49721.2021.9497215
full_text_status: none
publication: Proceedings - International Conference on Computer and Information Sciences: Sustaining Tomorrow with Digital Innovation, ICCOINS 2021
pagerange: 114-119
refereed: TRUE
isbn: 9781728171517
citation:   Sultana, S. and Hussain, S.S. and Hashmani, M. and Fayez, F.A. and Umair, M.  (2021) A Neoteric Variant of Deep Learning Network for Chest Radiograph Automated Annotation.  In: UNSPECIFIED.