Automation Characterization for Oral Cancer by Pathological Image Processing with Gray-Level Co-occurrence Matrix

Oral cancer is one of the most prevalent tumors of the head and neck region. As a result, reliable techniques for detecting are urgently required. Accordingly, the present study proposes an optical method using a Scanned Laser Pico-Projection system (SLPP) and Gray-Level Cooccurrence Matrix (GLCM). The validity of the proposed method is demonstrated using five indexes for quantized criteria for oral cancer classifies. The results show that the cancerous pathological sections have the higher Contrast and Entropy, but the lower Correlation, Homogeneity, and Energy. SLPP system with GLCM image processing can differentiate normal & cancerous pathological sections and it works on both full field analysis and specific tissue analysis. The discrimination of normal and cancerous tissues depends on the disorder caused by unusual proliferation and division of the chromosomes and nuclei. Compared to existing methods, the proposed method approach has many advantages, including a lower cost, a larger sample size and a more reliable diagnostic performance. As a result, it provides a highly promising solution for the pathologists/doctors.