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13-09-2022

A Survey on Convolutional Neural Networks for MRI Analysis

Authors: Shreya Hardaha, Damodar Reddy Edla, Saidi Reddy Parne

Published in: Wireless Personal Communications | Issue 2/2023

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Abstract

As of late, Convolutional Neural Networks have been very successful in segmentation and classification tasks. Magnetic resonance imaging (MRI) is a favored medical imaging method that comes up with interesting information for the diagnosis of different diseases.MR method is getting to be exceptionally well-known due to its non-invasive rule and for this reason, automated processing of this sort of image is getting noticed. MRI is effectively and widely used for tumor detection. Brain tumor detection is a popular medical application of MRI. Automating segmentation using CNN assists radiologists to lessen the high manual workload of tumor evaluation. CNN classification accuracy depends on network parameters and training data. CNN has the benefit of learning image features automatically directly out of multi-modal MRI images. In this survey paper, we have presented a summary of CNN's recent advancement in its technique applied on MRI images. The aim of this survey is to discuss various architectures and factors affecting the performance of CNN for learning features from different available MRI datasets. Based on the survey, section III (CNN for MRI analysis) comprises three subsections: A) MRI data and processing, B) CNN dimensionality, C) CNN architectures.

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Title: A Survey on Convolutional Neural Networks for MRI Analysis
Authors: Shreya Hardaha
Damodar Reddy Edla
Saidi Reddy Parne
Publication date: 13-09-2022
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 2/2023
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-022-09989-0

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