Grad- CAM and Grad-Cam++ visualization of optimized CNN.

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posted on 2025-03-25, 20:04 authored by Ali Raza, Akhtar Ali, Sami Ullah, Yasir Nadeem Anjum, Basit Rehman

Grad- CAM and Grad-Cam++ visualization of optimized CNN.

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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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