出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Oral cancer is one of the most widespread tumors of the head and neck region. An earlier
diagnosis can help dentist getting a better therapy plan, giving patients a better treatment and
the reliable techniques for detecting oral cancer cells are urgently required. This study
proposes an optic and automation method using reflection images obtained with scanned laser
pico-projection system, and Gray-Level Co-occurrence Matrix for sampling. Moreover, the
artificial intelligence technology, Support Vector Machine, was used to classify samples.
Normal Oral Keratinocyte and dysplastic oral keratinocyte were simulating the evolvement of
cancer to be classified. The accuracy in distinguishing two cells has reached 85.22%.
Compared to existing diagnosis methods, the proposed method possesses many advantages,
including a lower cost, a larger sample size, an instant, a non-invasive, and a more reliable
diagnostic performance. As a result, it provides a highly promising solution for the early
diagnosis of oral squamous carcinoma.
关键词：Oral Cancer Cell; Normal Oral Keratinocyte (NOK); Dysplastic oral keratinocyte (DOK);GrayLevel;
Co-occurrence Matrix (GLCM); Scanned Laser Pico-Projection (SLPP); Support Vector;
Machine (SVM); Machine-Learning
