Grad- CAM and Grad-Cam++ visualization of optimized CNN.
<p>Grad- CAM and Grad-Cam++ visualization of optimized CNN.</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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| الموضوعات: | |
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| _version_ | 1852021865942351872 |
|---|---|
| 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 | 2025-03-25T20:04:27Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0317181.g023 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Grad-_CAM_and_Grad-Cam_visualization_of_optimized_CNN_/28664948 |
| 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 | Grad- CAM and Grad-Cam++ visualization of optimized CNN. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Grad- CAM and Grad-Cam++ visualization of optimized CNN.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_eee1f5d73a75bbd540aeeffd61a1a604 |
| identifier_str_mv | 10.1371/journal.pone.0317181.g023 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28664948 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Grad- CAM and Grad-Cam++ visualization of optimized CNN.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>Grad- CAM and Grad-Cam++ visualization of optimized CNN.</p>2025-03-25T20:04:27ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0317181.g023https://figshare.com/articles/figure/Grad-_CAM_and_Grad-Cam_visualization_of_optimized_CNN_/28664948CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/286649482025-03-25T20:04:27Z |
| spellingShingle | Grad- CAM and Grad-Cam++ visualization of optimized CNN. 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 | Grad- CAM and Grad-Cam++ visualization of optimized CNN. |
| title_full | Grad- CAM and Grad-Cam++ visualization of optimized CNN. |
| title_fullStr | Grad- CAM and Grad-Cam++ visualization of optimized CNN. |
| title_full_unstemmed | Grad- CAM and Grad-Cam++ visualization of optimized CNN. |
| title_short | Grad- CAM and Grad-Cam++ visualization of optimized CNN. |
| title_sort | Grad- CAM and Grad-Cam++ visualization of optimized CNN. |
| 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 |