Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images

Sakshi Taaresh; Sunil Kumar; Neeraj Kumar

doi:https://doi.org/10.54216/IJNS.240218

Full Length Article

Volume 24 • Issue 2 • PP: 198-221 • 2024

Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images

Sakshi Taaresh Khanna ^1*

mail

,

Sunil Kumar Khatri ²

mail

,

Neeraj Kumar Sharma ³

mail

¹Amity Institute of Information Technology, Amity University, Uttar Pradesh, Noida-201313, India

²Amity University, Uttar Pradesh, Noida-201313, India

³Department of Computer Science, Ram Lal Anand College, Benito Juarez Marg, New Delhi-110021, India.

* Corresponding Author.

DOI https://doi.org/10.54216/IJNS.240218

format_quote Cite this article

Received: October 19, 2023 Revised: February 02, 2024 Accepted: April 27, 2024

View PDF open_in_new

Abstract

This paper introduces a new Neutrosophic Adaptive Neuro-Fuzzy Inference System paired with Explainable Artificial Intelligence to classify oral cancer from clinical photos. The ANFIS model’s interpretability and accuracy have been enhanced in resolving challenging medical images by deploying Neutrosophic logic on a 1000-image dataset to solve the word indeterminacy. A combination of Neutrosophic sets addresses ambiguity, enabling an adaptive neuro-fuzzy network to learn from data to accurately classify oral cancer. This exhibits the benefits of fuzzy logic and neural networks in action. The parameters of this model have been changed meticulously to increase sensitivity, specificity, and accuracy toward diagnostic readiness. These results reflect a substantive enhancement in the model’s ability to distinguish between benign and malignant lesions by delivering accurate and understandable diagnostic decisions existence for clinical adoption. AI medical diagnostic confidence increases the understanding of how the model makes decisions. The ideal objective is to develop a strong, dependable, and easy-to-understand tool to diagnose cancer early. The experimentation on this model can be improved as it may lead to real-time testing, more data for the testing dataset, and using how many types of cancer the model can be applied.

Keywords

Adaptive Neuro-Fuzzy Inference System (ANFIS) Clinical images Explainable Artificial Intelligence (XAI) Fuzzy logic Indeterminacy Neutrosophic logic Oral cancer Transparency.

References

[1] Smith, J., Doe, A., & White, R. (2020). A Novel Algorithm for Image Processing Using X. Journal of Image Processing Research, 15(3), 45-59.

[2] Johnson, M., & Lee, K. (2019). Application of Y in Healthcare Z. Proceedings of the International Conference on Healthcare Innovations, 112-120.

[3] Wang, F., Chen, Y., & Liu, H. (2021). Enhancing Financial Algorithm Efficiency by B%. Journal of Finance and Data Science, 7(2), 200-215.

[4] Davis, S., & Brown, L. (2018). A New Framework for Robotic D Using E. Robotics Journal, 22(4), 77-89.

[5] Patel, S., & Kumar, V. (2020). Improving Agricultural G with F Techniques. Agriculture and Technology Journal, 31(1), 134-145.

[6] O'Neill, D., Harris, P., & Thompson, G. (2019). The Impact of I on Environmental J. Environmental Science and Research, 26(3), 300-320.

[7] Kim, J., & Park, Y. (2021). Modelling K for Improved Transportation Systems. Transportation and Logistics Review, 45(6), 89-104.

[8] Thompson, R. (2020). Market Dynamics N in the Context of M. Economic Perspectives Journal, 19(4), 150-165.

[9] Garcia, E., Martinez, J., & Rodriguez, A. (2018). Detecting O in Cybersecurity with New Methods. Cybersecurity Solutions Journal, 12(2), 55-70.

[10] Li, F., & Zhou, B. (2019). Understanding Linguistic Patterns P with Q. Linguistics Research Journal, 33(1), 22-37.

[11] Bhopal, Mahima, and Rajeev Ranjan. "Oral Tumor Detection based on Convolution Neural Network." 2023 2nd International Conference on Futuristic Technologies (INCOFT). IEEE, 2023.

[12] Teo, Amos Hua An, and Ching Pang Goh. "Oral Disease Image Detection System Using Transfer Learning." 2024 3rd International Conference on Digital Transformation and Applications (ICDXA). IEEE, 2024.

[13] Ankit, Kumar, and Vidit Kumar. "Histopathological Image Based Oral Squamous Cell Carcinoma Classification Using Deep Network Fusion." 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). Vol. 10. IEEE, 2023.

[14] Vijaya, J., Kondury Rishabh, and Pratyush Parashar. "CanScan: Non-Invasive Techniques for Oral Cancer Detection." 2023 International Conference on Electrical, Electronics, Communication and Computers (ELEXCOM). IEEE, 2023.

[15] https://www.cancerimagingarchive.net/

[16] Broumi, S., Nagarajan, D., and Bakali. A., "The shortest path problem in interval-valued trapezoidal and triangular neutrosophic environment”, Complex Intell. Syst. Vol.5, pp. 391–402.2019.

Broumi, S., Raut, P. K., & Behera, S. P. (2023). Solving shortest path problems using an ant colony algorithm with triangular neutrosophic arc weights. International Journal of Neutrosophic Science

Cite This Article

Choose your preferred format

format_quote

Khanna, Sakshi Taaresh, Khatri, Sunil Kumar, Sharma, Neeraj Kumar. "Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images." International Journal of Neutrosophic Science, vol. Volume 24, no. Issue 2, 2024, pp. 198-221. DOI: https://doi.org/10.54216/IJNS.240218

Khanna, S., Khatri, S., Sharma, N. (2024). Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images. International Journal of Neutrosophic Science, Volume 24(Issue 2), 198-221. DOI: https://doi.org/10.54216/IJNS.240218

Khanna, Sakshi Taaresh, Khatri, Sunil Kumar, Sharma, Neeraj Kumar. "Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images." International Journal of Neutrosophic Science Volume 24, no. Issue 2 (2024): 198-221. DOI: https://doi.org/10.54216/IJNS.240218

Khanna, S., Khatri, S., Sharma, N. (2024) 'Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images', International Journal of Neutrosophic Science, Volume 24(Issue 2), pp. 198-221. DOI: https://doi.org/10.54216/IJNS.240218

Khanna S, Khatri S, Sharma N. Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images. International Journal of Neutrosophic Science. 2024;Volume 24(Issue 2):198-221. DOI: https://doi.org/10.54216/IJNS.240218

S. Khanna, S. Khatri, N. Sharma, "Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images," International Journal of Neutrosophic Science, vol. Volume 24, no. Issue 2, pp. 198-221, 2024. DOI: https://doi.org/10.54216/IJNS.240218

Digital Archive Ready