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...
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MDPI AG
2020-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/20/5780 |
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| author | Muhammad Awais Hemant Ghayvat Anitha Krishnan Pandarathodiyil Wan Maria Nabillah Ghani Anand Ramanathan Sharnil Pandya Nicolas Walter Mohamad Naufal Saad Rosnah Binti Zain Ibrahima Faye |
| author_facet | Muhammad Awais Hemant Ghayvat Anitha Krishnan Pandarathodiyil Wan Maria Nabillah Ghani Anand Ramanathan Sharnil Pandya Nicolas Walter Mohamad Naufal Saad Rosnah Binti Zain Ibrahima Faye |
| author_sort | Muhammad Awais |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T15:41:33Z |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:41:33Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-85c895577fc044a8a66c8212e9c2cbfa2023-11-20T16:47:46ZengMDPI AGSensors1424-82202020-10-012020578010.3390/s20205780Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence ImagingMuhammad Awais0Hemant Ghayvat1Anitha Krishnan Pandarathodiyil2Wan Maria Nabillah Ghani3Anand Ramanathan4Sharnil Pandya5Nicolas Walter6Mohamad Naufal Saad7Rosnah Binti Zain8Ibrahima Faye9Center for Intelligent Medical Electronics, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, ChinaInnovation Division Technical University of Denmark, 2800 Lyngby, DenmarkOral Diagnostic Sciences, Faculty of Dentistry, SEGi University, Jalan Teknologi, Kota Damansara, Petaling Jaya 47810, Selangor, MalaysiaOral Cancer Research and Coordinating Centre, Faculty of Dentistry, University of Malaya, Kuala Lumpur 50603, MalaysiaOral Cancer Research and Coordinating Centre, Faculty of Dentistry, University of Malaya, Kuala Lumpur 50603, MalaysiaSymbiosis Centre for Applied Artificial Intelligence and CSE Dept, Symbiosis International (Deemed) University, Pune 412115, Maharashtra, IndiaDepartment of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, MalaysiaDepartment of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, MalaysiaOral Cancer Research and Coordinating Centre, Faculty of Dentistry, University of Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, MalaysiaOral 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.https://www.mdpi.com/1424-8220/20/20/5780oral mucosal canceroral potentially malignant disorders (OPMD)oral cavity mucosal lesionsautofluorescence imagingtexture analysisVELscope<sup>®</sup> |
| spellingShingle | Muhammad Awais Hemant Ghayvat Anitha Krishnan Pandarathodiyil Wan Maria Nabillah Ghani Anand Ramanathan Sharnil Pandya Nicolas Walter Mohamad Naufal Saad Rosnah Binti Zain Ibrahima Faye 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 oral mucosal cancer oral potentially malignant disorders (OPMD) oral cavity mucosal lesions autofluorescence imaging texture analysis VELscope<sup>®</sup> |
| 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 | oral mucosal cancer oral potentially malignant disorders (OPMD) oral cavity mucosal lesions autofluorescence imaging texture analysis VELscope<sup>®</sup> |
| url | https://www.mdpi.com/1424-8220/20/20/5780 |
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