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Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm

Savareh, Behrouz Alizadeh and Emami, Hassan and Hajiabadi, Mohamadreza and Azimi, Seyedmajid and Ghafoori, Mahyar (2019) Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm. Biomedical Engineering - Biomedizinische Technik, pp. 1-11. de Gruyter. DOI: 10.1515/bmt-2017-0178 ISSN 0013-5585 (In Press)

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Official URL: http://dx.doi.org/10.1515/bmt-2017-0178

Abstract

Purpose: Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. Materials and methods: In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Results: Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Conclusion: Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

Item URL in elib:https://elib.dlr.de/124225/
Document Type:Article
Title:Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Savareh, Behrouz AlizadehShahid Beheshti University of Medical Sciences, Tehran, IranUNSPECIFIED
Emami, HassanShahid Beheshti University of Medical Sciences, Tehran, IranUNSPECIFIED
Hajiabadi, MohamadrezaTehran University of Medical Sciences, Tehran, IranUNSPECIFIED
Azimi, SeyedmajidSeyedmajid.Azimi (at) dlr.dehttps://orcid.org/0000-0002-6084-2272
Ghafoori, MahyarIran University of Medical SciencesUNSPECIFIED
Date:May 2019
Journal or Publication Title:Biomedical Engineering - Biomedizinische Technik
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1515/bmt-2017-0178
Page Range:pp. 1-11
Publisher:de Gruyter
ISSN:0013-5585
Status:In Press
Keywords:brain tumor, convolutional neural network, segmentation, wavelet transform
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Zielske, Mandy
Deposited On:19 Dec 2018 17:02
Last Modified:19 Dec 2018 17:02

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