Cellular Automata for Pattern Recognition

Wongthanavasu, Sartra; Ponkaew, Jetsada

doi:10.5772/52364

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Cellular Automata (CA) or cellular machine is a dynamic mathematical model, the model concepts were initially introduced by Neumann in 1966 (Wongthanavasu and Ponkaew, 2013). By following the cellular automata studied by Ulam, Conway (introduced the Game of life) and Wolfram (studied and presented the Wolfram rules) (Sarkar, 2000;Shiraishi et al, 2011).…”

Section: Background and Related Workmentioning

confidence: 99%

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

Hadavi¹,

Nordin²,

Shojaeipour³

et al. 2020

Journal of Computer Science

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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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Section: Background and Related Workmentioning

confidence: 99%

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

Hadavi¹,

Nordin²,

Shojaeipour³

et al. 2020

Journal of Computer Science

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Pattern Classification with Rejection Using Cellular Automata-Based Filtering

Jastrzębska

Sluzhenko

2017

Computer Information Systems and Industrial Management

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Part 1: AlgorithmsInternational audienceIn this article we address the problem of contaminated data in pattern recognition tasks, where apart from native patterns we may have foreign ones that do not belong to any native class. We present a novel approach to image classification with foreign pattern rejection based on cellular automata. The method is based only on native patterns, so no knowledge about characteristics of foreign patterns is required at the stage of model construction. The proposed approach is evaluated in a study of handwritten digits recognition. As foreign patterns we use distorted digits. Experiments show that the proposed model classifies native patterns with a high success rate and rejects foreign patterns as well

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