Histological Grading of Oral Tumors using Fuzzy Cognitive Map

K. Anuradha¹and K. P. Uma²

¹Department of MCA, Karpagam College of Engineering, Coimbatore.

²Department of Mathematics, Hindusthan College of Engineering and Technology, Coimbatore.

Corresponding Author E-mail: k_anur@yahoo.com

Abstract: Oral Tumor grading can be performed in many ways. To find the depth of the tumor, the conventional TNM (Tumor, Node, Metastatis) staging has been performed by experts for several years. But this staging system is not adequate for optimal prognostication and must be supplemented by different recent methods. This study uses Fuzzy Cognitive Maps (FCM) to grade oral tumors. Eight histopathological features were used to develop Fuzzy Cognitive Map model. Active Hebbian Learning (AHL), the supervised learning algorithm is used to train and improve the FCM’s grading. 123 cases containing 85 normal and 38 abnormal cases of oral tumor were used for testing. The proposed model (FCM and AHL) achieved an accuracy of 90.58% for oral tumors of low grade and 89.47% of high grade.

Keywords: Active Hebbian Learning Fuzzy Cognitive Map; Oral Tumor; Supervised Learning; TNM;

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