research-article

RETRACTED ARTICLE: Deep learning-based soft computing model for image classification application

Authors:

I. Jasmine Selvakumari Jeya,

S. N. DeepaAuthors Info & Claims

Soft Computing, Volume 24, Issue 24

Pages 18411 - 18430

https://doi.org/10.1007/s00500-020-05048-7

Published: 01 December 2020 Publication History

Abstract

The growth of swarm intelligence approaches and machine learning models in the field of medical image processing is extravagant, and the applicability of these approaches for various types of cancer classification has as well grown in the recent years. Considering the growth of these machine learning models, in this work attempt is taken to develop an optimized deep learning neural network classifier for classifying the nodule tissues in the lung cancer images which is an important application in biomedical area. The optimized model developed is the hybrid version of adaptive multi-swarm particle swarm optimizer with the new improved firefly algorithm resulting in better exploration and exploitation mechanism to determine near-optimal solutions. Multi-swarm particle swarm optimizer (MSPSO) possesses strong exploration capability due to its regrouping schedule nature, and the improved firefly algorithm (ImFFA) possesses better exploitation mechanism due to its inherit attractiveness and intensity feature. At this juncture, the new adaptive MSPSO–ImFFA is applied to the deep learning neural classifier to overcome the local and global minima occurrences and premature convergence by tuning its weight values. As a result, in this work the new adaptive MSPSO–ImFFA-based deep learning neural network classifier is employed to classify the lung cancer tissues of the considered lung computed tomography images. Results obtained prove the effectiveness of the deep learning classifier for the considered lung image sample datasets in comparison with the other methods compared from the previous literature works.

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Cited By

Mohammadian Takaloo VHashemzadeh MGhavidel Neycharan J(2023)DiagCovidPNA: diagnosing and differentiating COVID-19, viral and bacterial pneumonia from chest X-ray images using a hybrid specialized deep learning approachSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-08915-128:15-16(8657-8680)Online publication date: 9-Jul-2023
https://dl.acm.org/doi/10.1007/s00500-023-08915-1
Krishna SJeya IDeepa S(2022)Fuzzy-twin proximal SVM kernel-based deep learning neural network model for hyperspectral image classificationNeural Computing and Applications10.1007/s00521-022-07517-634:21(19343-19376)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00521-022-07517-6

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RETRACTED ARTICLE: Deep learning-based soft computing model for image classification application

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Published In

cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 24, Issue 24

Dec 2020

733 pages

ISSN:1432-7643

EISSN:1433-7479

Issue’s Table of Contents

© Springer-Verlag GmbH Germany, part of Springer Nature 2020. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 December 2020

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Cited By

Mohammadian Takaloo VHashemzadeh MGhavidel Neycharan J(2023)DiagCovidPNA: diagnosing and differentiating COVID-19, viral and bacterial pneumonia from chest X-ray images using a hybrid specialized deep learning approachSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-08915-128:15-16(8657-8680)Online publication date: 9-Jul-2023
https://dl.acm.org/doi/10.1007/s00500-023-08915-1
Krishna SJeya IDeepa S(2022)Fuzzy-twin proximal SVM kernel-based deep learning neural network model for hyperspectral image classificationNeural Computing and Applications10.1007/s00521-022-07517-634:21(19343-19376)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00521-022-07517-6

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