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

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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)

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.

Original languageEnglish
Article number5780
Pages (from-to)1-25
Number of pages25
JournalSensors (Switzerland)
Volume20
Issue number20
DOIs
Publication statusPublished - 2 Oct 2020
Externally publishedYes

Keywords

  • Autofluorescence imaging
  • Oral cavity mucosal lesions
  • Oral mucosal cancer
  • Oral potentially malignant disorders (OPMD)
  • Texture analysis
  • VELscope®

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