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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.

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.

Metastasis is responsible for 90% of all cancer-related deaths in humans, and the development of a rapid and promising solution for an early diagnosis of metastasis is required. The present study proposed a promising method combined with scanned laser pico-projection technique and typical texture feature (i.e., contrast, correlation, energy, entropy, and homogeneity) extraction of gray-level co-occurrence matrix (GLCM) image processing model to classify the low- and high-metastatic cancer cells using five common cancer adenocarcinoma cell line pairs (i.e., HeLa/HeLa-S3, CL1-0/CL1-5, OVTW59-P0/OVTW59-P4, and CE81T-FNlow/CE81T-FNhigh cell lines). Highly metastatic cancer cells essentially have the highest levels of disorder. Both contrast and entropy refer to the degree of disorder, and energy and homogeneity refer to the degree of uniformity. These four texture features can be effective evaluation indexes for disorder in cancer cells responding to metastatic ability. Texture feature extraction forms reflection images, which are recorded with scanned laser pico-projection system; they effectively bridge the gap in information derived from transmission images. The low- and high-metastatic cancer cells are statistically and effectively classified from the texture feature of GLCM through transmission and reflection images taken with scanned laser pico-projection system. In particular, it only requires several seconds after producing a confluent monolayer of cells and achieves the rapid method with a more reliable diagnostic performance for metastatic ability of cancer cells in vitro or ex vivo.

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.