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Semantic-Driven Architecture for Autonomic Management of Cyber-Physical Systems (CPS) for Industry 4.0 Read chapter Read first chapter

2019 | OriginalPaper | Chapter

MRI Brain Images Compression and Classification Using Different Classes of Neural Networks

Authors : Abdelhakim El Boustani, Essaid El Bachari

Published in: New Trends in Model and Data Engineering

Publisher: Springer International Publishing

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Abstract

The aim of this paper is to build an automatic system for compression and classification for magnetic resonance imaging brain images. The algorithm segments the images in order to separate regions of medical interest from its background. Only the regions of interest are compressed with a low-ratio scheme, while the rest of the image is compressed with a high-ratio scheme. Based on Convolutional Neural Network (CNN) method for classification and a Probabilistic Neural Network (PNN) for image segmentation, the system has been developed. Experiments were conducted to evaluate the performance of our approach using different optimizers with a huge dataset of MRI brain images. Results confirmed that the Root Mean Square Propagation (RMSprop) optimizer converges faster with a highest accuracy comparing to other optimizers and showed that the proposed preprocessing schema reduced the execution time.

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Metadata
Title
MRI Brain Images Compression and Classification Using Different Classes of Neural Networks
Authors
Abdelhakim El Boustani
Essaid El Bachari
Copyright Year
2019
Book
New Trends in Model and Data Engineering

Print ISBN: 978-3-030-32212-0

Electronic ISBN: 978-3-030-32213-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-32213-7_9

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