Textural pattern classification for oral squamous cell carcinoma

Textural pattern classification for oral squamous cell carcinoma

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 b...

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Bibliographic Details
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
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Summary: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.
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content type line 23
ISSN:0022-2720
1365-2818
DOI:10.1111/jmi.12611