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@article{184243,
        author = {Prof.Santhosh SG and Priyanka R},
        title = {Stage-wise Classification of Oral Diseases using DenseNet201 Deep Learning Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {745-752},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184243},
        abstract = {In order to improve patient outcomes and avoid serious dental consequences, early diagnosis of oral disorders is essential. A deep learning-based system for the automated categorization of two important oral diseases dental cavities (enamel caries) and gum disease (periodontitis) is presented in this paper. Using DenseNet201 as the backbone model, the system classifies disease stages into early, moderate, and advanced categories. Balanced dataset and preprocessing techniques were applied to improve model reliability. The framework was trained and tested on oral images, achieving an accuracy of 97.8% for gum disease and 89.8% for cavities. A graphical user interface (GUI) was developed to enable real-time image-based predictions, providing not only disease stage but also treatment recommendations for clinical support. The proposed system demonstrates the potential of deep learning in dental diagnostics and can serve as a supportive tool for dentists in decision-making and early intervention.},
        keywords = {Computer Vision, Deep Learning, Densenet201, Dental Caries, Oral Disease Detection, Periodontitis, Stage wise Diagnosis},
        month = {September},
        }