TY - JOUR
T1 - Healthcare professional in the loop (HPIL)
T2 - Classification of standard and oral cancer-causing anomalous regions of oral cavity using textural analysis technique in autofluorescence imaging
AU - Awais, Muhammad
AU - Ghayvat, Hemant
AU - Krishnan Pandarathodiyil, Anitha
AU - Nabillah Ghani, Wan Maria
AU - Ramanathan, Anand
AU - Pandya, Sharnil
AU - Walter, Nicolas
AU - Naufal Saad, Mohamad
AU - Zain, Rosnah Binti
AU - Faye, Ibrahima
N1 - Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/10/2
Y1 - 2020/10/2
N2 - 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.
AB - 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.
KW - Autofluorescence imaging
KW - Oral cavity mucosal lesions
KW - Oral mucosal cancer
KW - Oral potentially malignant disorders (OPMD)
KW - Texture analysis
KW - VELscope®
UR - http://www.scopus.com/inward/record.url?scp=85092667196&partnerID=8YFLogxK
U2 - 10.3390/s20205780
DO - 10.3390/s20205780
M3 - Article
C2 - 33053886
AN - SCOPUS:85092667196
SN - 1424-8220
VL - 20
SP - 1
EP - 25
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 20
M1 - 5780
ER -