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

Nooshin Hadavi,Md Jan Nordin,Ali Shojaeipour,Mohammad Faidzul Nasrudin

doi: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%).

Highlights

Pattern recognition is the study that identifies an object through a signal or image, based on its features which are obtained from a set of related data
The simple Classifier Cellular Automata (CCA) is compared with various decision tree approaches and the results indicated the CCA gives the best results in the case of medical data in comparison with other classifiers
According to the last image processing techniques which have been used for the lung Computed Tomography (CT)- scan images processing, we have selected a few methods which have better performance to apply on the target database

Summary

Introduction

Pattern recognition is the study that identifies an object through a signal or image, based on its features which are obtained from a set of related data. The pattern recognition approach has become a topic of interest in the study of medical diagnosis and employing the technique has added significant contributions to the advancement of this field. The type of required information is often not fixed and constant and some have relatively low-level features such as texture, shape and other pixels based on the statics extracted from the images and utilized for recognition (Homma, 2009). Automatic systems that are designed for medical diagnosis through the integration of computers and medical science are called Computer Aided Diagnosis (CAD)

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