ROC-AUC curve obtained by ML-IGPA (All classifiers).

<p>ROC-AUC curve obtained by ML-IGPA (All classifiers).</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Anwar Hossain Efat (19942283) (author)
مؤلفون آخرون:	S. M. Mahedy Hasan (19942286) (author), Md. Palash Uddin (19139222) (author), Md. Al Mamun (12800084) (author)
منشور في:	2024
الموضوعات:	Immunology Cancer Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified ham1000 dataset demonstrates dominance dispersion remains convolutional neural network approach achieves 94 achieving desired outcomes abnormal cell growth level ensemble approach identify responsible regions facilitating early diagnosis including skin diseases customized transfer learning approach &# 8220 igpa ),&# 8221 determining optimal weights model &# 8217 &# 8220 ensemble learning specific regions early detection skin cancer skin abnormalities machine learning deep learning triple attention surpassing state specifically involves primary objective novel method multiple levels integral component exact focus enhance explainability empirical evaluation el ). currently lacking current studies art methods affect structure
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author	Anwar Hossain Efat (19942283)
author2	S. M. Mahedy Hasan (19942286) Md. Palash Uddin (19139222) Md. Al Mamun (12800084)
author2_role	author author author
author_facet	Anwar Hossain Efat (19942283) S. M. Mahedy Hasan (19942286) Md. Palash Uddin (19139222) Md. Al Mamun (12800084)
author_role	author
dc.creator.none.fl_str_mv	Anwar Hossain Efat (19942283) S. M. Mahedy Hasan (19942286) Md. Palash Uddin (19139222) Md. Al Mamun (12800084)
dc.date.none.fl_str_mv	2024-10-24T17:36:09Z
dc.identifier.none.fl_str_mv	10.1371/journal.pone.0309430.g013
dc.relation.none.fl_str_mv	https://figshare.com/articles/figure/ROC-AUC_curve_obtained_by_ML-IGPA_All_classifiers_/27293936
dc.rights.none.fl_str_mv	CC BY 4.0 info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv	Immunology Cancer Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified ham1000 dataset demonstrates dominance dispersion remains convolutional neural network approach achieves 94 achieving desired outcomes abnormal cell growth level ensemble approach identify responsible regions facilitating early diagnosis including skin diseases customized transfer learning approach &# 8220 igpa ),&# 8221 determining optimal weights model &# 8217 &# 8220 ensemble learning specific regions early detection skin cancer skin abnormalities machine learning deep learning triple attention surpassing state specifically involves primary objective novel method multiple levels integral component exact focus enhance explainability empirical evaluation el ). currently lacking current studies art methods affect structure
dc.title.none.fl_str_mv	ROC-AUC curve obtained by ML-IGPA (All classifiers).
dc.type.none.fl_str_mv	Image Figure info:eu-repo/semantics/publishedVersion image
description	<p>ROC-AUC curve obtained by ML-IGPA (All classifiers).</p>
eu_rights_str_mv	openAccess
id	Manara_1de4ed94de2a0be0fca31f54d683ccbb
identifier_str_mv	10.1371/journal.pone.0309430.g013
network_acronym_str	Manara
network_name_str	ManaraRepo
oai_identifier_str	oai:figshare.com:article/27293936
publishDate	2024
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv	CC BY 4.0
spelling	ROC-AUC curve obtained by ML-IGPA (All classifiers).Anwar Hossain Efat (19942283)S. M. Mahedy Hasan (19942286)Md. Palash Uddin (19139222)Md. Al Mamun (12800084)ImmunologyCancerComputational BiologyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedham1000 dataset demonstratesdominance dispersion remainsconvolutional neural networkapproach achieves 94achieving desired outcomesabnormal cell growthlevel ensemble approachidentify responsible regionsfacilitating early diagnosisincluding skin diseasescustomized transfer learningapproach &# 8220igpa ),&# 8221determining optimal weightsmodel &# 8217&# 8220ensemble learningspecific regionsearly detectionskin cancerskin abnormalitiesmachine learningdeep learningtriple attentionsurpassing statespecifically involvesprimary objectivenovel methodmultiple levelsintegral componentexact focusenhance explainabilityempirical evaluationel ).currently lackingcurrent studiesart methodsaffect structure<p>ROC-AUC curve obtained by ML-IGPA (All classifiers).</p>2024-10-24T17:36:09ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0309430.g013https://figshare.com/articles/figure/ROC-AUC_curve_obtained_by_ML-IGPA_All_classifiers_/27293936CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/272939362024-10-24T17:36:09Z
spellingShingle	ROC-AUC curve obtained by ML-IGPA (All classifiers). Anwar Hossain Efat (19942283) Immunology Cancer Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified ham1000 dataset demonstrates dominance dispersion remains convolutional neural network approach achieves 94 achieving desired outcomes abnormal cell growth level ensemble approach identify responsible regions facilitating early diagnosis including skin diseases customized transfer learning approach &# 8220 igpa ),&# 8221 determining optimal weights model &# 8217 &# 8220 ensemble learning specific regions early detection skin cancer skin abnormalities machine learning deep learning triple attention surpassing state specifically involves primary objective novel method multiple levels integral component exact focus enhance explainability empirical evaluation el ). currently lacking current studies art methods affect structure
status_str	publishedVersion
title	ROC-AUC curve obtained by ML-IGPA (All classifiers).
title_full	ROC-AUC curve obtained by ML-IGPA (All classifiers).
title_fullStr	ROC-AUC curve obtained by ML-IGPA (All classifiers).
title_full_unstemmed	ROC-AUC curve obtained by ML-IGPA (All classifiers).
title_short	ROC-AUC curve obtained by ML-IGPA (All classifiers).
title_sort	ROC-AUC curve obtained by ML-IGPA (All classifiers).
topic	Immunology Cancer Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified ham1000 dataset demonstrates dominance dispersion remains convolutional neural network approach achieves 94 achieving desired outcomes abnormal cell growth level ensemble approach identify responsible regions facilitating early diagnosis including skin diseases customized transfer learning approach &# 8220 igpa ),&# 8221 determining optimal weights model &# 8217 &# 8220 ensemble learning specific regions early detection skin cancer skin abnormalities machine learning deep learning triple attention surpassing state specifically involves primary objective novel method multiple levels integral component exact focus enhance explainability empirical evaluation el ). currently lacking current studies art methods affect structure

ROC-AUC curve obtained by ML-IGPA (All classifiers).

مواد مشابهة