Lung infection detection and classification using the integration of the improved grasshopper and the remora optimization approaches with improved SVM

Infectious lung diseases, such as pneumonia and COVID-19, pose significant threats to global health, with high mortality rates and substantial burdens on healthcare systems. Accurate and timely diagnosis is crucial for effective management and treatment. This study addresses the limitations of exist...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Neural computing & applications Ročník 37; číslo 27; s. 22573 - 22591
Hlavný autor:	Bhimavarapu, Usharani
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.09.2025
Predmet:	Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Image Processing and Computer Vision Probability and Statistics in Computer Science S.I.: Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Special Issue on Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Grasshopper optimization Lung disease Rider optimization Multi-thresholding
ISSN:	0941-0643, 1433-3058
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Infectious lung diseases, such as pneumonia and COVID-19, pose significant threats to global health, with high mortality rates and substantial burdens on healthcare systems. Accurate and timely diagnosis is crucial for effective management and treatment. This study addresses the limitations of existing diagnostic methods by proposing advanced techniques based on computer-aided diagnosis systems and enhanced machine-learning algorithms. The methodology involves the development of novel algorithms for image enhancement, segmentation, feature selection, and classification. A kurtosis-based multi-thresholding grasshopper optimization algorithm is proposed for image segmentation, reducing complexity and enhancing the accuracy of lesion identification. An improved rider optimization algorithm is also introduced for feature selection, aiming to prioritize relevant features and reduce dimensionality effectively. Furthermore, an enhanced support vector machine (SVM) algorithm for lesion classification is presented, utilizing linear mapping to generate feature scores for regions of interest. This facilitates the evaluation of the loss function and improves classification results. The approach’s effectiveness is demonstrated using datasets comprising chest X-ray and CT scan images from the LIDC-IDRI and Montgomery datasets. The improved optimization algorithms were trained and tested over the chest X-ray and CT scan image datasets. An improved SVM classified the lesions with an accuracy of 99.9% for chest X-ray images and 99.8% for CT scan images. The results proved that the improved SVM adequately classifies lung diseases from the chest X-ray and CT scan images. The findings suggest that the proposed methodologies significantly enhance the accuracy and efficiency of diagnosing pneumonia and COVID-19 from medical images. By addressing the limitations of existing diagnostic techniques, this research contributes to improving healthcare practices and ultimately reducing the burden of infectious lung diseases on a global scale.
AbstractList	Infectious lung diseases, such as pneumonia and COVID-19, pose significant threats to global health, with high mortality rates and substantial burdens on healthcare systems. Accurate and timely diagnosis is crucial for effective management and treatment. This study addresses the limitations of existing diagnostic methods by proposing advanced techniques based on computer-aided diagnosis systems and enhanced machine-learning algorithms. The methodology involves the development of novel algorithms for image enhancement, segmentation, feature selection, and classification. A kurtosis-based multi-thresholding grasshopper optimization algorithm is proposed for image segmentation, reducing complexity and enhancing the accuracy of lesion identification. An improved rider optimization algorithm is also introduced for feature selection, aiming to prioritize relevant features and reduce dimensionality effectively. Furthermore, an enhanced support vector machine (SVM) algorithm for lesion classification is presented, utilizing linear mapping to generate feature scores for regions of interest. This facilitates the evaluation of the loss function and improves classification results. The approach’s effectiveness is demonstrated using datasets comprising chest X-ray and CT scan images from the LIDC-IDRI and Montgomery datasets. The improved optimization algorithms were trained and tested over the chest X-ray and CT scan image datasets. An improved SVM classified the lesions with an accuracy of 99.9% for chest X-ray images and 99.8% for CT scan images. The results proved that the improved SVM adequately classifies lung diseases from the chest X-ray and CT scan images. The findings suggest that the proposed methodologies significantly enhance the accuracy and efficiency of diagnosing pneumonia and COVID-19 from medical images. By addressing the limitations of existing diagnostic techniques, this research contributes to improving healthcare practices and ultimately reducing the burden of infectious lung diseases on a global scale.
Author	Bhimavarapu, Usharani
Author_xml	– sequence: 1 givenname: Usharani surname: Bhimavarapu fullname: Bhimavarapu, Usharani email: ushareddy@kluniversity.in organization: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation
BookMark	eNp9kMtOwzAQRS1UJNrCD7DKDwTsvBwvUcVLCmLBY2s59rhx1diRnYLgQ_he3KYSOzYzoztzrkZ3gWbWWUDokuArgjG9DhiXGUlxVqQEV_t6guakyPM0x2U9Q3PMiriuivwMLULYYIyLqi7n6KfZ2XVirAY5GmcTBeNxElYlcitCMNpIcZB2wcTjsYMIjLD2k-r0JPWDdx-gkqiH0LlhAH8w2S899M6LxA2j6c33xIkhAkJ2EJJPM3Z_Bi_vT-foVIttgItjX6K3u9vX1UPaPN8_rm6aVJKSkpSqQrWUtpSVLRValITQmgomcwBBKdSMUt0yUWtJMtZC3dYVkwqUFFRpyfIlyiZf6V0IHjQfvOmF_-IE832yfEqWx2T5IVlOIpRPUIjHdg2eb9zO2_jnf9QvZOuDVg
Cites_doi	10.1007/s12553-022-00700-8 10.1016/j.neucom.2017.02.040 10.1016/B978-0-323-90769-9.00011-6 10.1109/ICTAI.2009.60 10.1038/s41598-016-0023-2 10.1007/s10489-017-1019-8 10.1016/j.bbe.2021.05.013 10.1007/s10489-020-01826-w 10.1148/radiol.2462070712 10.1016/j.neucom.2015.05.044 10.1111/1754-9485.13273 10.1016/j.future.2007.05.003 10.1109/ICCTCT.2018.8551008 10.1007/s12652-017-0655-5 10.1007/s10044-021-00984-y 10.1109/TIM.2018.2836058 10.1016/S1473-3099(06)70411-X 10.1007/s00521-018-3824-3 10.1007/s11227-020-03566-7 10.1186/s12938-020-00831-x 10.3390/rs11091134 10.1007/s12652-022-04118-y 10.1016/j.bspc.2022.103791 10.1007/978-3-030-77246-8_2 10.1016/j.patcog.2020.107747 10.1145/3510413 10.14419/ijet.v7i2.26.12538 10.1109/ACCESS.2019.2891673
ContentType	Journal Article
Copyright	The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024 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.
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024 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.
DBID	AAYXX CITATION
DOI	10.1007/s00521-024-10624-1
DatabaseName	CrossRef
DatabaseTitle	CrossRef
DatabaseTitleList
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1433-3058
EndPage	22591
ExternalDocumentID	10_1007_s00521_024_10624_1
GroupedDBID	-Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBRH ABBXA ABDBE ABDBF ABDZT ABECU ABFSG ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABRTQ ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACSTC ACUHS ACZOJ ADHHG ADHIR ADHKG ADIMF ADKFA ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AEZWR AFBBN AFDZB AFEXP AFGCZ AFHIU AFKRA AFLOW AFOHR AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGQPQ AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHPBZ AHSBF AHWEU AHYZX AIAKS AIGIU AIIXL AILAN AITGF AIXLP AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG ATHPR AVWKF AXYYD AYFIA AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PHGZM PHGZT PQGLB PT4 PT5 PUEGO QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 ZMTXR ~8M ~EX AAYXX AFFHD CITATION
ID	FETCH-LOGICAL-c1571-7d4db77b795b7afa511787a9c3eea77e8977fb9a8fc129be8b869cdedca7dfc93
IEDL.DBID	RSV
ISSN	0941-0643
IngestDate	Sat Nov 29 07:31:29 EST 2025 Tue Sep 09 01:10:40 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	27
Keywords	Grasshopper optimization Lung disease Rider optimization Multi-thresholding
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c1571-7d4db77b795b7afa511787a9c3eea77e8977fb9a8fc129be8b869cdedca7dfc93
PageCount	19
ParticipantIDs	crossref_primary_10_1007_s00521_024_10624_1 springer_journals_10_1007_s00521_024_10624_1
PublicationCentury	2000
PublicationDate	20250900 2025-09-00
PublicationDateYYYYMMDD	2025-09-01
PublicationDate_xml	– month: 9 year: 2025 text: 20250900
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London
PublicationTitle	Neural computing & applications
PublicationTitleAbbrev	Neural Comput & Applic
PublicationYear	2025
Publisher	Springer London
Publisher_xml	– name: Springer London
References	VK Gunjan (10624_CR28) 2022; 12 K Senthil Kumar (10624_CR26) 2019; 2019 H Liang (10624_CR17) 2019; 7 10624_CR20 D Binu (10624_CR24) 2018; 68 S Akter (10624_CR1) 2014; 36 10624_CR21 A Oulefki (10624_CR18) 2021; 114 10624_CR22 DM Hansell (10624_CR4) 2008; 246 S Ahuja (10624_CR7) 2021; 51 SZ Mirjalili (10624_CR25) 2018; 48 M Shanid (10624_CR33) 2020; 32 H Jia (10624_CR11) 2019; 11 SH Kassania (10624_CR35) 2021; 41 CFGD Santos (10624_CR23) 2022; 54 Y Xu (10624_CR27) 2022; 77 K Pradhan (10624_CR29) 2022; 14 S Dai (10624_CR15) 2015; 168 10624_CR9 DA Moses (10624_CR6) 2021; 65 10624_CR8 A Forestiero (10624_CR13) 2008; 24 KS Manic (10624_CR16) 2016; 9 Y Shen (10624_CR2) 2016; 6 S Perumal (10624_CR31) 2018; 7 SK Obaro (10624_CR3) 2006; 6 A Narin (10624_CR5) 2021; 24 10624_CR34 10624_CR14 10624_CR36 R Selvanambi (10624_CR32) 2020; 32 L Duan (10624_CR10) 2021; 77 L He (10624_CR12) 2017; 240 10624_CR19 M Prabukumar (10624_CR30) 2019; 10 L Hussain (10624_CR37) 2020; 19
References_xml	– volume: 12 start-page: 1197 issue: 6 year: 2022 ident: 10624_CR28 publication-title: Heal Technol doi: 10.1007/s12553-022-00700-8 – volume: 240 start-page: 152 year: 2017 ident: 10624_CR12 publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.02.040 – ident: 10624_CR8 doi: 10.1016/B978-0-323-90769-9.00011-6 – ident: 10624_CR14 doi: 10.1109/ICTAI.2009.60 – ident: 10624_CR20 – volume: 6 start-page: 20 issue: 1 year: 2016 ident: 10624_CR2 publication-title: Sci Rep doi: 10.1038/s41598-016-0023-2 – volume: 36 start-page: 97 issue: 2 year: 2014 ident: 10624_CR1 publication-title: Malays J Pathol – volume: 9 start-page: 89949 issue: 12 year: 2016 ident: 10624_CR16 publication-title: Ind J Sci Technol – volume: 2019 start-page: 4909846 issue: 1 year: 2019 ident: 10624_CR26 publication-title: Comput Math Methods Med – ident: 10624_CR22 – volume: 48 start-page: 805 year: 2018 ident: 10624_CR25 publication-title: Appl Intell doi: 10.1007/s10489-017-1019-8 – volume: 41 start-page: 867 issue: 3 year: 2021 ident: 10624_CR35 publication-title: Biocybern Biomed Eng doi: 10.1016/j.bbe.2021.05.013 – volume: 51 start-page: 571 year: 2021 ident: 10624_CR7 publication-title: Appl Intell doi: 10.1007/s10489-020-01826-w – volume: 246 start-page: 697 issue: 3 year: 2008 ident: 10624_CR4 publication-title: Radiology doi: 10.1148/radiol.2462070712 – volume: 168 start-page: 799 year: 2015 ident: 10624_CR15 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.05.044 – volume: 65 start-page: 498 issue: 5 year: 2021 ident: 10624_CR6 publication-title: J Med Imaging Radiat Oncol doi: 10.1111/1754-9485.13273 – volume: 24 start-page: 235 issue: 3 year: 2008 ident: 10624_CR13 publication-title: Futur Gener Comput Syst doi: 10.1016/j.future.2007.05.003 – ident: 10624_CR9 doi: 10.1109/ICCTCT.2018.8551008 – volume: 10 start-page: 267 year: 2019 ident: 10624_CR30 publication-title: J Ambient Intell Humaniz Comput doi: 10.1007/s12652-017-0655-5 – volume: 24 start-page: 1207 year: 2021 ident: 10624_CR5 publication-title: Pattern Anal Appl doi: 10.1007/s10044-021-00984-y – volume: 68 start-page: 2 issue: 1 year: 2018 ident: 10624_CR24 publication-title: IEEE Trans Instrum Meas doi: 10.1109/TIM.2018.2836058 – volume: 6 start-page: 150 issue: 3 year: 2006 ident: 10624_CR3 publication-title: Lancet Infect Dis doi: 10.1016/S1473-3099(06)70411-X – volume: 32 start-page: 4373 year: 2020 ident: 10624_CR32 publication-title: Neural Comput Appl doi: 10.1007/s00521-018-3824-3 – volume: 77 start-page: 6734 issue: 7 year: 2021 ident: 10624_CR10 publication-title: J Supercomput doi: 10.1007/s11227-020-03566-7 – volume: 19 start-page: 1 year: 2020 ident: 10624_CR37 publication-title: Biomed Eng Online doi: 10.1186/s12938-020-00831-x – ident: 10624_CR21 – volume: 11 start-page: 1134 issue: 9 year: 2019 ident: 10624_CR11 publication-title: Remote Sens doi: 10.3390/rs11091134 – volume: 14 start-page: 12933 issue: 9 year: 2022 ident: 10624_CR29 publication-title: J Ambient Intell Humanized Comput doi: 10.1007/s12652-022-04118-y – volume: 77 year: 2022 ident: 10624_CR27 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2022.103791 – ident: 10624_CR34 – ident: 10624_CR36 doi: 10.1007/978-3-030-77246-8_2 – volume: 32 start-page: 2050001 issue: 01 year: 2020 ident: 10624_CR33 publication-title: Biomed Eng: Appl, Basis Commun – ident: 10624_CR19 – volume: 114 year: 2021 ident: 10624_CR18 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2020.107747 – volume: 54 start-page: 1 issue: 10s year: 2022 ident: 10624_CR23 publication-title: ACM Computing Surveys (CSUR) doi: 10.1145/3510413 – volume: 7 start-page: 74 issue: 226 year: 2018 ident: 10624_CR31 publication-title: Int J Eng Technol doi: 10.14419/ijet.v7i2.26.12538 – volume: 7 start-page: 11258 year: 2019 ident: 10624_CR17 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2891673
SSID	ssj0004685
Score	2.389935
Snippet	Infectious lung diseases, such as pneumonia and COVID-19, pose significant threats to global health, with high mortality rates and substantial burdens on...
SourceID	crossref springer
SourceType	Index Database Publisher
StartPage	22573
SubjectTerms	Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Image Processing and Computer Vision Probability and Statistics in Computer Science S.I.: Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Special Issue on Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications
Title	Lung infection detection and classification using the integration of the improved grasshopper and the remora optimization approaches with improved SVM
URI	https://link.springer.com/article/10.1007/s00521-024-10624-1
Volume	37
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwELagMLBQnqK85IENIjUkjZ0RISoGqBCFqlt0ti_AQFI1hZ_C7-XsxIVKCAmWKHKsi-XzPWzf3cfYiUTVBQtookWiA9J-QCIlIEg0mW-dRqihBpsQg4Ecj9O7Jims8tHu_krSaep5sps9waSt73lMqiOxz2W2QuZOWsCG--HoWzakA-KkfYuN6YmjJlXmZxqL5mjxLtSZmH77f4PbYOuNS8kv6jWwyZaw2GJtD9fAG-ndZh83JNjcR18V3OCseYPCcG3daBs35FjFbTz8EyfvkPuCEra1zOsmdxKBhlN7VT2Xkwn9xxKxH6c2dhd4SbrotUny5L5yOVbcHvx-ERiObnfYY__q4fI6aGAZAh32RBgIExslhBJpTwnIgVw2knpIdYQIQqAklzJXKchckzOhUCqZpNqg0SBMTvzfZa2iLHCP8a5dDCjDHLAXg4ykAgQg6hb9LFZRh5167mSTuvpGNq-z7KY8oynP3JRnYYedeeZkjSRWv3Tf_1v3A7Z2brF_XXzZIWvNpm94xFb1--ylmh67JfgJ7ora9Q
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS8MwEA86BX1xfuL8zINvWljXrkkfRRyK2xA3x97KJU3VB9uxTv8U_14vaTIdiKAvpaThGnK5jyS_uyPkjCvRBF3QRLJIeqj9AEWKgRdJNN8yDpSEqtgE6_f5eBzf26Cw0qHd3ZWk0dTzYDd9golb31aIqiPSz2WyEqLF0hnzHwajb9GQphAn7ls0picMbKjMzzQWzdHiXagxMZ36_wa3STasS0kvqzWwRZZUvk3qrlwDtdK7Qz66KNjUoa9ymqqZfYM8pVK70Ro3ZFhFNR7-iaJ3SF1CCd1aZFWTOYlQKcX2snwuJhP8jyaiP041dhdogbro1QZ5Upe5XJVUH_x-ERiMervksXM9vLrxbFkGT_pt5nssDVPBmGBxWzDIAF02lHqIZaAUMKY4upSZiIFnEp0JobjgUSxTlUpgaYb83yO1vMjVPqFNvRgU9zNQ7RB4wAUoAKSuq5-FImiQc8edZFJl30jmeZbNlCc45YmZ8sRvkAvHnMRKYvlL94O_dT8lazfDXjfp3vbvDsl6S9cBNlizI1KbTd_UMVmV77OXcnpiluMnOyrd2Q
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEF60inixPrE-9-BNQx9Ju5ujqEWxlkK19BZmH1EPpqWp_hR_rzObpLYggngJYRMmYXdmdnb3m_kYO5NW1YAITbRoaQ-9H6BJCfBaGqdvHfpWQ0Y2IbpdORyGvbksfod2L44ks5wGqtKUTKtjE1dniW-0m4nL4EaAbqRF12W2EhCQntbr_cFcZqQj5cQ1DOF7Aj9Pm_lZxuLUtHgu6qabdvn_P7rJNvJQk19murHFlmyyzcoFjQPPrXqHfXbQ4HmBykq4sdP8DhLDNYXXhCdyQ8gJJ__MMWrkRaEJah3FWZPbobCGY3uavozGY_wOCaGHE8L0Ah-hj3rLkz95UdHcppw2hL8F9AcPu-ypffN4devldA2erjdF3RMmMEoIJcKmEhADhnLoDSDUvrUghJUYasYqBBlrDDKUlUq2Qm2s0SBMjHqxx0rJKLH7jNdISaysx2CbAUhfKrAAKJ1Y0QLlV9h5MVLROKvKEc3qL7suj7DLI9flUb3CLoqBinILTX95_eBvr5-ytd51O-rcde8P2XqD6IEdBO2IlaaTd3vMVvXH9DWdnDjN_ALOZea9
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Lung+infection+detection+and+classification+using+the+integration+of+the+improved+grasshopper+and+the+remora+optimization+approaches+with+improved+SVM&rft.jtitle=Neural+computing+%26+applications&rft.au=Bhimavarapu%2C+Usharani&rft.date=2025-09-01&rft.pub=Springer+London&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=37&rft.issue=27&rft.spage=22573&rft.epage=22591&rft_id=info:doi/10.1007%2Fs00521-024-10624-1&rft.externalDocID=10_1007_s00521_024_10624_1
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon