Performance metric of optimized CNN with Adam for β = 1 .

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Bibliographic Details
Main Author:	Ali Raza (3558965) (author)
Other Authors:	Akhtar Ali (603199) (author), Sami Ullah (613609) (author), Yasir Nadeem Anjum (20934626) (author), Basit Rehman (20934629) (author)
Published:	2025
Subjects:	Medicine Immunology Developmental Biology Cancer Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified smart healthcare application mathematical computational cost healthcare providers diagnosing convolutional neural networks evaluate model interpretability skin cancer classification cancer skin classification div >< p cross validation technique classification accuracy rates >) could lead skin cancer class model training involved develop reliable optimized skin cancer model ’ skin lesions healthy class xai </ would assist unseen data unseed data turn leads strongly associated skin disease seven forms rmsprop </ prevalent types prediction models patient ’ optimized cnn offered specifically network fitting much smaller key aspect interpretational aspects initial diagnosis holdout validation healthare expenditure ham10000 </ generalization performance generalization ability enhanced speed early detection drawbacks mainly detected early convergence rate cnn </ clinical examination central component cam </ cam ++</ better performance better outcomes algorithms applied af </ adam </ activation functions >, achieving
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author	Ali Raza (3558965)
author2	Akhtar Ali (603199) Sami Ullah (613609) Yasir Nadeem Anjum (20934626) Basit Rehman (20934629)
author2_role	author author author author
author_facet	Ali Raza (3558965) Akhtar Ali (603199) Sami Ullah (613609) Yasir Nadeem Anjum (20934626) Basit Rehman (20934629)
author_role	author
dc.creator.none.fl_str_mv	Ali Raza (3558965) Akhtar Ali (603199) Sami Ullah (613609) Yasir Nadeem Anjum (20934626) Basit Rehman (20934629)
dc.date.none.fl_str_mv	2025-03-25T20:04:09Z
dc.identifier.none.fl_str_mv	10.1371/journal.pone.0317181.g008
dc.relation.none.fl_str_mv	https://figshare.com/articles/figure/Performance_metric_of_optimized_CNN_with_Adam_for_i_i_1_/28664903
dc.rights.none.fl_str_mv	CC BY 4.0 info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv	Medicine Immunology Developmental Biology Cancer Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified smart healthcare application mathematical computational cost healthcare providers diagnosing convolutional neural networks evaluate model interpretability skin cancer classification cancer skin classification div >< p cross validation technique classification accuracy rates >) could lead skin cancer class model training involved develop reliable optimized skin cancer model ’ skin lesions healthy class xai </ would assist unseen data unseed data turn leads strongly associated skin disease seven forms rmsprop </ prevalent types prediction models patient ’ optimized cnn offered specifically network fitting much smaller key aspect interpretational aspects initial diagnosis holdout validation healthare expenditure ham10000 </ generalization performance generalization ability enhanced speed early detection drawbacks mainly detected early convergence rate cnn </ clinical examination central component cam </ cam ++</ better performance better outcomes algorithms applied af </ adam </ activation functions >, achieving
dc.title.none.fl_str_mv	Performance metric of optimized CNN with Adam for <i>β</i> = 1 .
dc.type.none.fl_str_mv	Image Figure info:eu-repo/semantics/publishedVersion image
description	<p>Performance metric of optimized CNN with Adam for <i>β</i> = 1 .</p>
eu_rights_str_mv	openAccess
id	Manara_06f4e5cdd1f56579cf2e9a46cb0ee492
identifier_str_mv	10.1371/journal.pone.0317181.g008
network_acronym_str	Manara
network_name_str	ManaraRepo
oai_identifier_str	oai:figshare.com:article/28664903
publishDate	2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
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rights_invalid_str_mv	CC BY 4.0
spelling	Performance metric of optimized CNN with Adam for <i>β</i> = 1 .Ali Raza (3558965)Akhtar Ali (603199)Sami Ullah (613609)Yasir Nadeem Anjum (20934626)Basit Rehman (20934629)MedicineImmunologyDevelopmental BiologyCancerScience PolicySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsmart healthcare applicationmathematical computational costhealthcare providers diagnosingconvolutional neural networksevaluate model interpretabilityskin cancer classificationcancer skin classificationdiv >< pcross validation techniqueclassification accuracy rates>) could leadskin cancer classmodel training involveddevelop reliable optimizedskin cancermodel ’skin lesionshealthy classxai </would assistunseen dataunseed dataturn leadsstrongly associatedskin diseaseseven formsrmsprop </prevalent typesprediction modelspatient ’optimized cnnoffered specificallynetwork fittingmuch smallerkey aspectinterpretational aspectsinitial diagnosisholdout validationhealthare expenditureham10000 </generalization performancegeneralization abilityenhanced speedearly detectiondrawbacks mainlydetected earlyconvergence ratecnn </clinical examinationcentral componentcam </cam ++</better performancebetter outcomesalgorithms appliedaf </adam </activation functions>, achieving<p>Performance metric of optimized CNN with Adam for <i>β</i> = 1 .</p>2025-03-25T20:04:09ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0317181.g008https://figshare.com/articles/figure/Performance_metric_of_optimized_CNN_with_Adam_for_i_i_1_/28664903CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/286649032025-03-25T20:04:09Z
spellingShingle	Performance metric of optimized CNN with Adam for <i>β</i> = 1 . Ali Raza (3558965) Medicine Immunology Developmental Biology Cancer Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified smart healthcare application mathematical computational cost healthcare providers diagnosing convolutional neural networks evaluate model interpretability skin cancer classification cancer skin classification div >< p cross validation technique classification accuracy rates >) could lead skin cancer class model training involved develop reliable optimized skin cancer model ’ skin lesions healthy class xai </ would assist unseen data unseed data turn leads strongly associated skin disease seven forms rmsprop </ prevalent types prediction models patient ’ optimized cnn offered specifically network fitting much smaller key aspect interpretational aspects initial diagnosis holdout validation healthare expenditure ham10000 </ generalization performance generalization ability enhanced speed early detection drawbacks mainly detected early convergence rate cnn </ clinical examination central component cam </ cam ++</ better performance better outcomes algorithms applied af </ adam </ activation functions >, achieving
status_str	publishedVersion
title	Performance metric of optimized CNN with Adam for <i>β</i> = 1 .
title_full	Performance metric of optimized CNN with Adam for <i>β</i> = 1 .
title_fullStr	Performance metric of optimized CNN with Adam for <i>β</i> = 1 .
title_full_unstemmed	Performance metric of optimized CNN with Adam for <i>β</i> = 1 .
title_short	Performance metric of optimized CNN with Adam for <i>β</i> = 1 .
title_sort	Performance metric of optimized CNN with Adam for <i>β</i> = 1 .
topic	Medicine Immunology Developmental Biology Cancer Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified smart healthcare application mathematical computational cost healthcare providers diagnosing convolutional neural networks evaluate model interpretability skin cancer classification cancer skin classification div >< p cross validation technique classification accuracy rates >) could lead skin cancer class model training involved develop reliable optimized skin cancer model ’ skin lesions healthy class xai </ would assist unseen data unseed data turn leads strongly associated skin disease seven forms rmsprop </ prevalent types prediction models patient ’ optimized cnn offered specifically network fitting much smaller key aspect interpretational aspects initial diagnosis holdout validation healthare expenditure ham10000 </ generalization performance generalization ability enhanced speed early detection drawbacks mainly detected early convergence rate cnn </ clinical examination central component cam </ cam ++</ better performance better outcomes algorithms applied af </ adam </ activation functions >, achieving

Performance metric of optimized CNN with Adam for <i>β</i> = 1 .

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