Cargando…
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
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...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7601168/ https://www.ncbi.nlm.nih.gov/pubmed/33053886 http://dx.doi.org/10.3390/s20205780 |
_version_ | 1783603340646547456 |
---|---|
author | Awais, Muhammad Ghayvat, Hemant Krishnan Pandarathodiyil, Anitha Nabillah Ghani, Wan Maria Ramanathan, Anand Pandya, Sharnil Walter, Nicolas Saad, Mohamad Naufal Zain, Rosnah Binti Faye, Ibrahima |
author_facet | Awais, Muhammad Ghayvat, Hemant Krishnan Pandarathodiyil, Anitha Nabillah Ghani, Wan Maria Ramanathan, Anand Pandya, Sharnil Walter, Nicolas Saad, Mohamad Naufal Zain, Rosnah Binti Faye, Ibrahima |
author_sort | Awais, Muhammad |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7601168 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76011682020-11-01 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 Awais, Muhammad Ghayvat, Hemant Krishnan Pandarathodiyil, Anitha Nabillah Ghani, Wan Maria Ramanathan, Anand Pandya, Sharnil Walter, Nicolas Saad, Mohamad Naufal Zain, Rosnah Binti Faye, Ibrahima Sensors (Basel) Article 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. MDPI 2020-10-12 /pmc/articles/PMC7601168/ /pubmed/33053886 http://dx.doi.org/10.3390/s20205780 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Awais, Muhammad Ghayvat, Hemant Krishnan Pandarathodiyil, Anitha Nabillah Ghani, Wan Maria Ramanathan, Anand Pandya, Sharnil Walter, Nicolas Saad, Mohamad Naufal Zain, Rosnah Binti Faye, Ibrahima 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 |
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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7601168/ https://www.ncbi.nlm.nih.gov/pubmed/33053886 http://dx.doi.org/10.3390/s20205780 |
work_keys_str_mv | AT awaismuhammad healthcareprofessionalintheloophpilclassificationofstandardandoralcancercausinganomalousregionsoforalcavityusingtexturalanalysistechniqueinautofluorescenceimaging AT ghayvathemant healthcareprofessionalintheloophpilclassificationofstandardandoralcancercausinganomalousregionsoforalcavityusingtexturalanalysistechniqueinautofluorescenceimaging AT krishnanpandarathodiyilanitha healthcareprofessionalintheloophpilclassificationofstandardandoralcancercausinganomalousregionsoforalcavityusingtexturalanalysistechniqueinautofluorescenceimaging AT nabillahghaniwanmaria healthcareprofessionalintheloophpilclassificationofstandardandoralcancercausinganomalousregionsoforalcavityusingtexturalanalysistechniqueinautofluorescenceimaging AT ramanathananand healthcareprofessionalintheloophpilclassificationofstandardandoralcancercausinganomalousregionsoforalcavityusingtexturalanalysistechniqueinautofluorescenceimaging AT pandyasharnil healthcareprofessionalintheloophpilclassificationofstandardandoralcancercausinganomalousregionsoforalcavityusingtexturalanalysistechniqueinautofluorescenceimaging AT walternicolas healthcareprofessionalintheloophpilclassificationofstandardandoralcancercausinganomalousregionsoforalcavityusingtexturalanalysistechniqueinautofluorescenceimaging AT saadmohamadnaufal healthcareprofessionalintheloophpilclassificationofstandardandoralcancercausinganomalousregionsoforalcavityusingtexturalanalysistechniqueinautofluorescenceimaging AT zainrosnahbinti healthcareprofessionalintheloophpilclassificationofstandardandoralcancercausinganomalousregionsoforalcavityusingtexturalanalysistechniqueinautofluorescenceimaging AT fayeibrahima healthcareprofessionalintheloophpilclassificationofstandardandoralcancercausinganomalousregionsoforalcavityusingtexturalanalysistechniqueinautofluorescenceimaging |