Research Article Open Access

Classification of Normal and Abnormal Lung CT-scan Images Using Cellular Learning Automata

Nooshin Hadavi1, Md Jan Nordin1, Ali Shojaeipour1 and Mohammad Faidzul Nasrudin1
  • 1 Universiti Kebangsaan Malaysia (UKM), Malaysia
Journal of Computer Science
Volume 16 No. 1, 2020, 14-24

DOI: https://doi.org/10.3844/jcssp.2020.14.24

Submitted On: 4 April 2017 Published On: 15 January 2020

How to Cite: Hadavi, N., Nordin, M. J., Shojaeipour, A. & Nasrudin, M. F. (2020). Classification of Normal and Abnormal Lung CT-scan Images Using Cellular Learning Automata. Journal of Computer Science, 16(1), 14-24. https://doi.org/10.3844/jcssp.2020.14.24

Abstract

This paper proposes a medical pattern recognition system based on the Cellular automata (CA). CA or cellular machine is a dynamic mathematical model that consists of several similar and simple units organized by considerably simple local rules. Each cell acts as a simple computer automaton. This can lead to the implementation of the complex computations through uncomplicated methods. However, the CA model needs to determine certain rules for specific use and this model is regarded as suitable for modelling certain systems. To overcome this problem, a method is needed through which the favorable rules are extracted. Cellular Learning Automata (CLA) model is obtained from developing CA by appending a Learning Automaton (LA) to each cell. Many applications of CA are known today, especially in the field of pattern recognition. Therefore in this study, we use the CLA to design an automatic system to diagnosis the images which contain cancer tissue. Hence in this study, after applying the required approaches on lung Computed Tomography (CT) images, images are classified through the CLA model and the proposed methods are evaluated in terms of sensitivity, specificity and accuracy. The proposed system promises a flexible and low complexity model. The method has been tested on 22 slices of CT scan images from a real-world dataset and has yielded satisfactory results. The model with a low error rate (0.09), yielded a favorable accuracy (95.4%).

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Keywords

  • Computer-Aided Diagnosis
  • Image Processing
  • Pattern Recognition
  • Classification
  • Cellular Learning Automata
  • Lung Cancer