Abstract: The most prevalent type of head and neck cancer is oral or mouth neoplasm, namely oral squamous cell carcinoma (OSCC).Despite its impact on mortality, it is invariably diagnosed late due to the ineffectiveness of early detection screening techniques. Early detection and treatment of oral squamous cell carcinoma (OSCC) is crucial for improved patient outcomes. Deep learning (DL) offers a promising approach for automated OSCC detection and classification. DL models can extract complex features from histopathological image dataset, achieving high accuracy in OSCC detection and classification. Studies have demonstrated DL is effective in distinguishing OSCC from benign lesions and classifying OSCC into different stages. DL-based OSCC detection and classification can improve diagnostic accuracy and efficiency, leading to earlier detection and treatment. However, further research is needed to validate DL models' clinical performance and ensure data quality and model interpretability. Overall, DL holds promise for revolutionizing OSCC diagnosis and management, enabling more accurate and personalized patient care.
Keywords: Deep learning(DL), Convolutional Neural Networks (CNNs), Oral Squamous Cell Carcinoma (OSCC), Histopathological
Cite:
Ravinarayana B, Ananya , Aparna P, Divija, Eeksha Jain, "ORAL SQUAMOUS CELL CARCINOMA DETECTION USING DEEP LEARNING ON HISTOPATHOLOGICAL IMAGES", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.133104.
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DOI:
10.17148/IJARCCE.2024.133104
