* The architecture employs standard Keras CNN layers, utilizing dropout for essential overfitting mitigation crucial in medical imaging diagnostics. * Crucially, the current model structure lacks Batch Normalization layers, which would significantly enhance training stability and convergence speed. * For reliable skin cancer classification accuracy, consider integrating deeper convolutional blocks or leveraging advanced transfer learning frameworks. * Input image processing pipeline is correctly established at 224x224x3, aligning well with common pre-trained weight standards like ImageNet. * The externalized JSON definition ensures robust model configuration persistence, simplifying deployment and version control procedures.
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