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International Journal of Neutrosophic Science

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https://doi.org/10.54216/IJNS

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International Journal of Neutrosophic Science

Volume 24 , Issue 2 , PP: 198-221, 2024 | Cite this article as | XML | Html | PDF

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

Sakshi Taaresh Khanna 1 * , Sunil Kumar Khatri 2 , Neeraj Kumar Sharma 3

  • 1 Amity Institute of Information Technology, Amity University, Uttar Pradesh, Noida-201313, India - (sakshitaareshkhanna@gmail.com; )
  • 2 Amity University, Uttar Pradesh, Noida-201313, India - (skkhatri@amity.edu;)
  • 3 Department of Computer Science, Ram Lal Anand College, Benito Juarez Marg, New Delhi-110021, India. - (neerajksharma100@gmail.com)

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

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

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    Cite This Article As :
    Sakshi Taaresh Khanna, Sunil Kumar Khatri, Neeraj Kumar Sharma. "Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images." Full Length Article, Vol. 24, No. 2, 2024 ,PP. 198-221 (Doi   :  https://doi.org/10.54216/IJNS.240218)
    Sakshi Taaresh Khanna, Sunil Kumar Khatri, Neeraj Kumar Sharma. (2024). Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images. Journal of , 24 ( 2 ), 198-221 (Doi   :  https://doi.org/10.54216/IJNS.240218)
    Sakshi Taaresh Khanna, Sunil Kumar Khatri, Neeraj Kumar Sharma. "Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images." Journal of , 24 no. 2 (2024): 198-221 (Doi   :  https://doi.org/10.54216/IJNS.240218)
    Sakshi Taaresh Khanna, Sunil Kumar Khatri, Neeraj Kumar Sharma. (2024). Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images. Journal of , 24 ( 2 ), 198-221 (Doi   :  https://doi.org/10.54216/IJNS.240218)
    Sakshi Taaresh Khanna, Sunil Kumar Khatri, Neeraj Kumar Sharma. Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images. Journal of , (2024); 24 ( 2 ): 198-221 (Doi   :  https://doi.org/10.54216/IJNS.240218)
    Sakshi Taaresh Khanna, Sunil Kumar Khatri, Neeraj Kumar Sharma, Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images, Journal of , Vol. 24 , No. 2 , (2024) : 198-221 (Doi   :  https://doi.org/10.54216/IJNS.240218)

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