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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
Fudan University, China.ORCID iD: 0000-0002-0379-0744
University of Denmark, Denmark.
SEGi University, Malaysia.ORCID iD: 0000-0002-5436-0138
University of Malaya, Malaysia.
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2020 (English)In: Sensors, E-ISSN 1424-8220, Vol. 20, no 20, article id 5780Article in journal (Refereed) Published
Sustainable development
SDG 3: Ensure healthy lives and promote well-being for all at all ages
Abstract [en]

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 differentiate 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.

Place, publisher, year, edition, pages
MDPI, 2020. Vol. 20, no 20, article id 5780
National Category
Bioinformatics and Computational Biology Dentistry
Research subject
Health and Caring Sciences, Health Informatics
Identifiers
URN: urn:nbn:se:lnu:diva-119188DOI: 10.3390/s20205780ISI: 000585674400001Scopus ID: 2-s2.0-85092667196OAI: oai:DiVA.org:lnu-119188DiVA, id: diva2:1735292
Available from: 2023-02-08 Created: 2023-02-08 Last updated: 2025-02-05Bibliographically approved

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Pandya, Sharnil

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Awais, MuhammadKrishnan Pandarathodiyil, AnithaRamanathan, AnandPandya, Sharnil
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