eprintid: 12661
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/26/61
datestamp: 2023-11-10 03:27:13
lastmod: 2023-11-10 03:27:13
status_changed: 2023-11-10 01:49:13
type: article
metadata_visibility: show
creators_name: Awais, M.
creators_name: Ghayvat, H.
creators_name: Krishnan Pandarathodiyil, A.
creators_name: Nabillah Ghani, W.M.
creators_name: Ramanathan, A.
creators_name: Pandya, S.
creators_name: Walter, N.
creators_name: Naufal Saad, M.
creators_name: Zain, R.B.
creators_name: Faye, I.
title: Healthcare professional in the loop (HPIL): Classification of standard and oral cancer-causing anomalous regions of oral cavity using textural analysis technique in autofluorescence imaging
ispublished: pub
keywords: Discriminant analysis; Feature extraction; Hough transforms; Nearest neighbor search, Circular Hough transforms; Feature selection algorithm; Gray level co occurrence matrix(GLCM); Health care professionals; K nearest neighbor (KNN); Linear discriminant analysis; Oral squamous cell carcinomata; Receiver operating characteristics, Health care, diagnostic imaging; fluorescence imaging; health care delivery; human; mouth tumor; squamous cell carcinoma; standard, Carcinoma, Squamous Cell; Delivery of Health Care; Humans; Mouth Neoplasms; Optical Imaging; Reference Standards
note: cited By 30
abstract: Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to diffierentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche�Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83, 85, and 84, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2020
publisher: MDPI AG
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092667196&doi=10.3390%2fs20205780&partnerID=40&md5=8dbf07aa15557c09531c911baec4f040
id_number: 10.3390/s20205780
full_text_status: none
publication: Sensors (Switzerland)
volume: 20
number: 20
pagerange: 1-25
refereed: TRUE
issn: 14248220
citation:   Awais, M. and Ghayvat, H. and Krishnan Pandarathodiyil, A. and Nabillah Ghani, W.M. and Ramanathan, A. and Pandya, S. and Walter, N. and Naufal Saad, M. and Zain, R.B. and Faye, I.  (2020) Healthcare professional in the loop (HPIL): Classification of standard and oral cancer-causing anomalous regions of oral cavity using textural analysis technique in autofluorescence imaging.  Sensors (Switzerland), 20 (20).  pp. 1-25.  ISSN 14248220