Performance comparison of various machine learning classifiers using fusion of LBP, intensity and GLCM feature extraction techniques for thyroid nodules classification Online publication date: Mon, 19-Feb-2024
by Rajshree Srivastava; Pardeep Kumar
International Journal of Grid and Utility Computing (IJGUC), Vol. 15, No. 1, 2024
Abstract: Machine Learning (ML) and feature extraction techniques have shown a great potential in medical imaging field. This work presents an effective approach for the identification and classification of thyroid nodules. In the proposed model, various features are extracted using Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and intensity-based matrix. These features are fed to various ML classifiers like K-Nearest Neighbour (KNN), Decision-Tree (DT), Artificial Neural Network (ANN), Naïve Bayes, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Linear Regression (LR) and Support Vector Machine (SVM). From the result analysis, it can be observed that proposed Model-4 has performed better in comparison with the rest of seven proposed models with the reported literature. An improvement of 4% to 5% is seen in performance evaluation of model in comparison with reported literature.
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