Textural pattern classification for oral squamous cell carcinoma

• Published in: Journal of microscopy (Oxford) Vol. 269; no. 1; pp. 85 - 93
• Main Authors: RAHMAN, T.Y., MAHANTA, L.B., CHAKRABORTY, C., DAS, A.K., SARMA, J.D.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01-01-2018
• Subjects: Automation; Biopsy; Cancer; Classifiers; Diagnosis; Feature extraction; Fibrosis; GLCM; Health risk assessment; histogram; Incidence; Matrices (mathematics); Medical diagnosis; Oral cancer; Oral squamous cell carcinoma; Pattern classification; PCA; SCC; Squamous cell carcinoma; SVM; texture; t‐test; histogram; SCC; Biopsy; texture; t-test; SVM; GLCM; oral cancer; PCA
• ISSN: 0022-2720; 1365-2818
• DOI: 10.1111/jmi.12611

Summary
Despite being an area of cancer with highest worldwide incidence, oral cancer yet remains to be widely researched. Studies on computer‐aided analysis of pathological slides of oral cancer contribute a lot to the diagnosis and treatment of the disease. Some researches in this direction have been carried out on oral submucous fibrosis. In this work an approach for analysing abnormality based on textural features present in squamous cell carcinoma histological slides have been considered. Histogram and grey‐level co‐occurrence matrix approaches for extraction of textural features from biopsy images with normal and malignant cells are used here. Further, we have used linear support vector machine classifier for automated diagnosis of the oral cancer, which gives 100% accuracy.
Lay description
Despite being an area of cancer with highest worldwide incidence, oral cancer yet remains to be widely researched. Studies on computer‐aided analysis of pathological slides of oral cancer contribute a lot to the diagnosis and treatment of the disease. Some researches in this direction have been carried out on oral submucous fibrosis. In this work an approach for analysing abnormality based on textural features present in squamous cell carcinoma histological slides have been considered. Histogram and grey‐level co‐occurrence Matrix approaches for extraction of textural features from biopsy images with normal and malignant cells are used here. Further, we have used linear support vector machine classifier for automated diagnosis of the oral cancer, which gives 100% accuracy.