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Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Integrated System for Oral Cancer Early Detection

A.I.R HettiarachchiDepartment of Computer Science & Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri LankaDayarathna H.R.N.CDepartment of Computer Science & Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri LankaSeran M.NDepartment of Computer Science & Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri LankaThathsarani K.P.HDepartment of Information & Technology, Sri Lanka Institute of Information Technology, Malabe, Sri LankaMs. Suriyaa KumariDepartment of Computer Science & Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri LankaMr. N.H.P. Ravi Supunya SwarnakanthaDepartment of Computer Science & Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Vol 7 No 10 (2023): Volume 7, Issue 10, October 2023 | Pages: 601-608

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 05-11-2023

doi Logo doi.org/10.47001/IRJIET/2023.710080

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Abstract

The integration of an oral cancer early detection and staging system through the implementation of a mobile application is the focus of this research paper. The mobile app comprises three main components: 1) detecting oral cancer using lips and tongue images, 2) oral cancer detection using CT scan images, 3) oral cancer detection employing histopathological images and assessing the severity of the patient's cancer using medical data. Convolutional Neural Networks (CNNs) were utilized to train models for the first two parts, while logistic regression was employed to determine the severity of patients' conditions. This paper presents a comprehensive study on these integrated approaches with promising results in advancing early detection and accurate staging methods for oral cancer patients.

Keywords

Histopathology, Convolutional Neural Networks (CNNs), Logistic Regression, OCScanner, Artificial Intelligence


Citation of this Article

A.I.R Hettiarachchi, Dayarathna H.R.N.C, Seran M.N, Thathsarani K.P.H, Ms. Suriyaa Kumari, Mr. N.H.P. Ravi Supunya Swarnakantha, “Integrated System for Oral Cancer Early Detection” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 10, pp 601-608, October 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.710080

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. J. Musulin, D. Štifanić, A. Zulijani, S. B. Šegota and I. Lorencin , "Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods," in The 21st IEEE International Conference on BioInformatics and BioEngineering, 2021.
  2. H. Lin , H. Chen, L. Weng, J. Shao and J. Lin, "Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis.," Journal of Biomedical Optics., vol. 26, no. 8, 2021.
  3. A.-u. Rahman , . A. Alqahtani, N. Aldhafferi, . M. Umar Nasir, M. Farhan Khan and M. Adnan Khan, "Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning.," Sensors , vol. 3833, no. 22, 2022.
  4. X.A. López-Cortés, F. Matamala, B. Venegas and C. Rivera, "Machine-Learning Applications in Oral Cancer: A Systematic Review," Applied Sciences, vol. 5715, no. 12, 2022.
  5. N. Das , E. Hussain and L. B. Mahanta , "Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network.," Neural Networks, Vols. 128,, pp. 47-60.
  6. T. Yesmin Rahman, L. B. Mahanta, A. K. Das and J. D. Sarma, "Histopathological imaging database for oral cancer analysis.," Data in Brief, 2020.
  7. S. Panigrahi and T. Swarnkar, "Machine Learning Techniques used for the Histopathological Image Analysis of Oral Cancer-A Review," The Open Bioinformatics Journal, vol. 13, pp. 106-117, 2020.
  8. A.Alhazmi, Y. Alhazmi, A. Masmali, N. Salawi, K. Masmali and A. Makrami, "Application of artificial intelligence and machine learning for prediction of oral cancer risk.," Oral Pathology and Medicine - WILEY, 2020.
  9. C. S Chu, N. P Lee, J. Adeoye, . P. Thomson and S.-W. Choi , "Machine learning and treatment outcome prediction for oral cancer.," Journal of Oral Pathology & Medicine, vol. 49, no. 10, pp. 977-985, 2020.
  10. N. Jmour, S. Zayen and A. Abdelkrim, "Convolutional neural networks for image classification.," in International Conference on Advanced Systems and Electric Technologies, 2018.
  11. H. Yu, L. T. Yang and Q. Zhang, "Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives," Neurocomputing, vol. 444, pp. 92-110, 2021.

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