Development of an Automated Medical Diagnosis System for Classifying Thyroid Tumor Cells using Multiple Classifier Fusion

Abstract

An automated medical diagnosis system has been developed to discriminate benign and malignant thyroid nodules in multi-stained fine needle aspiration biopsy (FNAB) images using multiple classifier fusion and presented in this paper. First, thyroid cell regions are extracted from the auto-cropped sub-image by implementing mathematical morphology segmentation method. Subsequently, statistical features are extracted by two-level wavelet decomposition based on texture characteristics of the thyroid cells. After that, decision tree (DT), k-nearest neighbor ( k-NN), Elman neural network (ENN) and support vector machine (SVM) classifiers are used separately to classify thyroid nodules into benign and malignant. The four individual classifier outputs are then fused together using majority voting rule and linear combination rules to improve the performance of the diagnostic system. The classification results of ENN and SVM classifiers show an overall diagnostic accuracy (DA) of 90%, sensitivity (Se) of 85% and 100%, specificity (Sp) of 90% and 90% respectively. However, the best diagnostic accuracy of 96.66% is obtained by multiple classifier fusion with majority voting rule and linear combination rules. The experimental results show that the proposed method is a useful tool for the diagnosis of thyroid cancer and can provide a second opinion for a physician’s decision.