Cellular Neural Networks Templates Learning Approach Based on Mutual Information and Firefly Algorithm for X-Ray Images de-noising | ||||
Mansoura Journal for Computer and Information Sciences | ||||
Volume 15, Issue 2, December 2019, Page 57-67 PDF (1.87 MB) | ||||
Document Type: Original Research Articles. | ||||
DOI: 10.21608/mjcis.2019.321068 | ||||
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Authors | ||||
Ahmed I. Sharaf* 1; Mohamed E. Abu El-Soud1; Ibrahim M. El-Henawy2 | ||||
1Faculty of Computers & Information Systems, Dept. of Computer Science, Mansoura University, Egypt | ||||
2Faculty of Computers & Information Systems, Dept. of Computer Science, Zagazig University, Egypt | ||||
Abstract | ||||
The Cellular Neural Network is a 2D array of analog processors which forms a parallel computing framework. The main key factors in this model are the values of the neighborhood of each cell, which are called templates. These templates are usually set by a domain expert in this framework to determine the optimal values of the templates. In this paper, a novel approach was proposed to discover the templates of the cellular neural networks based on mutual information and firefly optimization. The mutual information discovers the hidden pattern in the templates by measuring the similarities among cells. The firefly algorithm navigates the search space to find the optimal values of the templates. The benchmarking and validation have been performed on the ChestX-ray8, which is a real-world X-ray images dataset. The proposed method achieved significant results when compared to other meta-heuristics algorithms such as Genetic Algorithm and Particle Swarm Optimization. | ||||
Keywords | ||||
Cellular Neural Networks; Medical Images; X-Ray Images; Firefly Algorithm; Evolutionary Algorithms | ||||
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