Open Access

ARTICLE

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

by Mustafa Musa Jaber^1,2,*, Salman Yussof¹, Amer S. Elameer³, Leong Yeng Weng¹, Sura Khalil Abd^2,6, Anand Nayyar^4,5

1 Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Malaysia
2 Department of Computer Science, Dijlah University Collage, Baghdad, 10021, Iraq
3 Biomedical Informatics College, University of Information Technology and Communications, Baghdad, Iraq
4 Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Viet Nam
5 Graduate School, Duy Tan University, Da Nang, 550000, Viet Nam
6 Department of Computer Science, Al-turath University College, Baghdad, Iraq

* Corresponding Author: Mustafa Musa Jaber. Email:

Computers, Materials & Continua 2022, 72(2), 2175-2190. https://doi.org/10.32604/cmc.2022.023387

Received 06 September 2021; Accepted 31 December 2021; Issue published 29 March 2022

Abstract

Artificial intelligence plays an essential role in the medical and health industries. Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis. However, convolution networks examine medical images effectively; such systems require high computational complexity when recognizing the same disease-affected region. Therefore, an optimized deep convolution network is utilized for analyzing disease-affected regions in this work. Different disease-related medical images are selected and examined pixel by pixel; this analysis uses the gray wolf optimized deep learning network. This method identifies affected pixels by the gray wolf hunting process. The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis. The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule. The pattern-matching process recognizes the disease effectively, and the system's efficiency is evaluated using the MATLAB implementation process. This process ensures high accuracy of up to 99.02% to 99.37% and reduces computational complexity.

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Keywords

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