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- DOI 10.18231/j.jds.76024.1759127383
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Automated detection of oral potential malignant disorders using exfoliative cytology
Background: Oral exfoliative cytology serves as a non-invasive diagnostic tool for detecting cellular abnormalities. However, manual analysis can be time-consuming and prone to subjective interpretation. With advancements in Artificial Intelligence (AI), automated systems offer promising solutions for improving diagnostic accuracy and efficiency.
Aim: This study aims to evaluate the effectiveness of AI-based techniques, including machine learning and deep learning models, for classifying normal and abnormal oral exfoliative cells through cytomorphological analysis.
Materials and Methods: The study employed two AI approaches. The first involved use of cellular and nuclear dimensions, such as cell and nuclear diameters, which were analyzed using a Decision Tree classifier. The second method utilized a deep learning model based on the AlexNet Convolutional Neural Network (CNN) architecture for image-based classification. Grad-CAM visualizations were used to identify biologically significant regions contributing to the classification.
Results: The Decision Tree classifier, based on cytomorphometric features, achieved an accuracy of 100% in distinguishing normal from abnormal cells. The AlexNet-based CNN model achieved a classification accuracy of 93%. Grad-CAM results provided interpretability by highlighting relevant morphological areas in the cytological images.
Conclusion: The study demonstrates that integrating cytological morphometry with AI techniques significantly enhances the accuracy and interpretability of oral cell abnormality detection. These findings support the potential of AI-assisted cytology as a reliable tool for early screening of oral pathologies.
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How to Cite This Article
Vancouver
Zainab H, Sultana A, Pardeshi R. Automated detection of oral potential malignant disorders using exfoliative cytology [Internet]. J Dent Spec. 2025 [cited 2025 Oct 01];13(2):210-214. Available from: https://doi.org/10.18231/j.jds.76024.1759127383
APA
Zainab, H., Sultana, A., Pardeshi, R. (2025). Automated detection of oral potential malignant disorders using exfoliative cytology. J Dent Spec, 13(2), 210-214. https://doi.org/10.18231/j.jds.76024.1759127383
MLA
Zainab, Heena, Sultana, Ameena, Pardeshi, Rajmohan. "Automated detection of oral potential malignant disorders using exfoliative cytology." J Dent Spec, vol. 13, no. 2, 2025, pp. 210-214. https://doi.org/10.18231/j.jds.76024.1759127383
Chicago
Zainab, H., Sultana, A., Pardeshi, R.. "Automated detection of oral potential malignant disorders using exfoliative cytology." J Dent Spec 13, no. 2 (2025): 210-214. https://doi.org/10.18231/j.jds.76024.1759127383