Abstract
Artificial Intelligence (AI) has gained enormous prominence for recent years and is becoming a core technology in various fields. The use of AI is becoming increasingly prevalent in medical diagnosis. Many algorithms have been developed to assist the physicians and doctors in their clinical screening work. In this study, a comparative analysis of six widely used machine learning algorithms [Support Vector Machine (SVM), Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN), Neural network, Decision Tree] has been done to find the most suitable model for the classification of oral cancer. The effectiveness of the ML methods are compared and evaluated in terms of accuracy, precision and recall using histopathological images of oral cancer. The experimental results shows that, Neural Network outperformed the other models by giving accuracy of 90.4%. Thus, this model can be used as a tool for the screening of binary classification of oral histopathological images.
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Panigrahi, S., Nanda, B.S., Swarnkar, T. (2022). Comparative Analysis of Machine Learning Algorithms for Histopathological Images of Oral Cancer. In: Sahoo, J.P., Tripathy, A.K., Mohanty, M., Li, KC., Nayak, A.K. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-16-4807-6_31
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