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@article{193763,
        author = {Mrs.R. Sujatha and P. Venkataramana Reddy and Nageli Chaitanya Kumar and Maski Harika and K Kapil and Melingi Kavya},
        title = {ORAL CANCER DETECTION USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1415-1424},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193763},
        abstract = {Oral cancer is a life-threatening disease in which early identification significantly improves treatment success and patient survival. Traditional screening methods, which rely primarily on visual examination and laboratory investigations, can be time-intensive and may fail to detect early-stage abnormalities. This study presents a deep learning-based framework for automated oral cancer detection using tongue images. A transfer learning approach is employed using the DenseNet169 architecture to perform multi-class classification of oral conditions, including oral cancer, leukoplakia, thrush, lichen, hairy tongue, and healthy tongue. Data augmentation techniques were incorporated to enhance model robustness and generalization. The proposed DenseNet based model achieved an accuracy of 94.08%, significantly outperforming the conventional LeNet model. Additionally, a lightweight CNN model was developed and deployed through a Django-based web application to support real-time binary classification (Cancer / Non-Cancer). The system provides a scalable, efficient, and user-friendly solution for automated oral disease detection.},
        keywords = {Oral Cancer, Deep Learning, CNN, DenseNet169, Transfer Learning, Medical Image Classification, Django, Early Detection, Artificial Intelligence in Healthcare},
        month = {March},
        }