Cross validation of skin cancer data with prediction accuracy of optimized CNN.

Cross validation of skin cancer data with prediction accuracy of optimized CNN.

<p>Cross validation of skin cancer data with prediction accuracy of optimized CNN.</p>

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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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Summary:	<p>Cross validation of skin cancer data with prediction accuracy of optimized CNN.</p>

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