Advanced deep learning and transfer learning approaches for breast cancer classification using advanced multi-line classifiers and datasets with model optimization and interpretability.

This is corroborated by an ROC-AUC of 0.992, very tight upper and lower bounds for the accuracy of 97.76% and 99.76%, and a highly significant p -value <0.001 reflected by its confusion matrix (71 TN, 0 FP, 2 FN, 41 TP).

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