LAUSR

Oral cancer is among the top 10 causes of death due to cancer worldwide. The prognosis for oral cancer patients is not good, with a 5-year survival rate of only 50%. Earlier and more precise classification will help clinicians make a diagnosis and patients survive. With the advancement of technology, computer-aided detection methods are used to help clinicians form therapy strategies. Gray-level co-occurrence matrix (GLCM) feature extraction of images describing the spatial distribution of gray levels is widely used in medical imaging analysis. Scanned laser pico projector (SLPP) has advantages such as high intensity, directivity, coherence, and mono-color with low bandwidth. In this study, GLCM feature extraction and SLPP reflex images were combined to make a small, non-staining, noninvasive classification system. According to the various image characteristics in oral carcinogenesis, SLPP reflex images better define the borders and three-dimensional structures and provide effective GLCM features such as contrast, energy, and homogeneity to classify carcinogenesis in dysplastic oral keratinocyte (DOK) and normal oral keratinocyte (NOK) cells. Moreover, it also reliably classifies highly metastatic (HSC-3) and tongue cancer (CAL-27) cells. A promising computer-aided classification system for oral cancer was developed to build a reliable intraoral examination system for in situ computer-aided diagnosis in normal clinics.