Machine-Learning with Cellular Automata

Povalej, Petra; Kokol, Peter; Družovec, Tatjana Welzer; Stiglic, Bruno

doi:10.1007/11552253_28

Cited by 5 publications

(1 citation statement)

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“…The simple Classifier Cellular Automata (CCA) is compared with various decision tree approaches (greedy, genetically and boosting) and the results indicated the CCA gives the best results in the case of medical data in comparison with other classifiers. Povalej et al (2005) also used the CA to solve a classification problem in another study by combining the various classifiers. In this system, the successful information in the classifiers is stored in the corresponding cells.…”

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%