Publication: Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm
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Date
2022
Authors
Jaber M.M.
Yussof S.
Elameer A.S.
Weng L.Y.
Abd S.K.
Nayyar A.
Journal Title
Journal ISSN
Volume Title
Publisher
Tech Science Press
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-relatedmedical 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. � 2022 Tech Science Press. All rights reserved.
Description
Automation; Complex networks; Computational complexity; Deep learning; Image analysis; Medical imaging; Pattern matching; Pixels; Distribution pattern-matching rule; Distribution patterns; Gray wolf-optimized deep convolution network; Gray wolves; Learning patterns; Matching rules; Medical fields; Medical image analysis; Pattern matching algorithms; Pattern-matching; Convolution