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Evaluating pre-trained convolutional neural networks for skin lesion classification using dermatoscopic and histopathological image datasets demonstrates that model performance differs between dermatoscopic and histopathological image modalities, showing that architectures exhibiting similar performance on dermatoscopic images exhibit different performance on histopathological data.
Achieving an RPD greater than 2.0 with low overfitting, this machine learning approach revolutionizes rapid soil nutrient quantification, crucial for sustainable agriculture.
