Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Supervised Classification of Normal and Tumorous Brain MR Images Using Machine Learning Schemes

Version 1 : Received: 28 July 2020 / Approved: 29 July 2020 / Online: 29 July 2020 (10:14:43 CEST)

How to cite: Lim, J.Q.; Alias, N.; Johar, F. Supervised Classification of Normal and Tumorous Brain MR Images Using Machine Learning Schemes. Preprints 2020, 2020070688 (doi: 10.20944/preprints202007.0688.v1). Lim, J.Q.; Alias, N.; Johar, F. Supervised Classification of Normal and Tumorous Brain MR Images Using Machine Learning Schemes. Preprints 2020, 2020070688 (doi: 10.20944/preprints202007.0688.v1).

Abstract

Manual interpretation of these huge amounts of image volumes are susceptible to inter-reader variability and human error. Thus, accurate automated CAD scheme is highly desirable in clinical pathological diagnosis. In this research, plethora of machine learning paradigms (e.g. feature extraction, dimensionality reduction and supervised classification methods) were explored, evaluated, compared and analyzed to identify the optimal pathway for brain MR images (normal vs neoplastic) binary classification task. External validation dataset was used to test the generalizability of the optimal predictive models implemented. Relevant and informative features were selected to construct cross-validated decision tree and eventually simple rule set was built based on the decision tree. The experimental results show that almost all pattern recognition paradigms achieve high accuracy with careful selection of number of attributes. LDA+ELM with 55 features are the optimal pipelines which achieve perfect classification when training and test data are of same source; and achieving (accuracy=97.5%, AUC=0.989, sensitivity=95% and specificity=100%) under balanced test dataset; (accuracy=99.5%, AUC=0.988, sensitivity=95% and specificity=100%). Cross-validated decision tree model also shows comparable performance: accuracy=98.8%, AUC=99.1%, sensitivity=99.6% and specificity=98.2%. Three highly relevant and robust attributes are visualized and selected for construction of decision tree models and finally a rule sets are read directly off the decision tree. This rule sets can potentially serve as fast and accurate classification algorithm.

Supplementary and Associated Material

Subject Areas

Computer aided diagnosis (CAD), brain magnetic resonance imaging (MRI) scans, feature extraction, feature reduction, classifiers, classification rule.

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