International Journal of Neutrosophic Science
IJNS
2690-6805
2692-6148
10.54216/IJNS
https://www.americaspg.com/journals/show/2771
2020
2020
Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images
Amity Institute of Information Technology, Amity University, Uttar Pradesh, Noida-201313, India
Sakshi
Sakshi
Amity University, Uttar Pradesh, Noida-201313, India
Sunil Kumar
Khatri
Department of Computer Science, Ram Lal Anand College, Benito Juarez Marg, New Delhi-110021, India.
Neeraj Kumar
Sharma
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.
2024
2024
198
221
10.54216/IJNS.240218
https://www.americaspg.com/articleinfo/21/show/2771