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2020 | OriginalPaper | Chapter

Random Subspace Combined LDA Based Machine Learning Model for OSCC Classifier

Authors : Archana Nawandhar, Navin Kumar, Lakshmi Yamujala

Published in: Modeling, Machine Learning and Astronomy

Publisher: Springer Singapore

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Abstract

Oral squamous cell carcinoma (OSCC) remains a major death causing oral cancer in developing countries. In recent years, tremendous development in medical imaging devices made microscopic colour images of biopsy samples available to the researchers. Image processing and machine learning techniques can be used to develop automatic cancer grading mechanism. In this work, automatic OSCC classifier using Linear Discriminant Analysis combined with Random Subspace is developed and analyzed. The proposed classifiers automatically classifies the input image in one of the four categories, namely: Normal, Grade-I, II or III. Total 83 colour and texture features are computed from the 100 Haemotoxylin and Eosin (H&E) stained images of oral mucosa. The overall accuracy of the proposed classifier is 93.5% with sensitivity and specificity of 0.89 and 0.95 respectively.

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Title: Random Subspace Combined LDA Based Machine Learning Model for OSCC Classifier
Authors: Archana Nawandhar
Navin Kumar
Lakshmi Yamujala
Publisher: Springer Singapore
Book: Modeling, Machine Learning and Astronomy
Print ISBN: 978-981-336-462-2

Electronic ISBN: 978-981-336-463-9

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-981-33-6463-9_3

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