A pruned and parameter-efficient Xception framework for skin cancer classification
Fig 5
Confusion matrix of the proposed model evaluated on the HAM10000 dataset.
All data augmentation and SMOTE procedures were applied exclusively to the training set after the train-test split, ensuring that the test set remained free of synthetic samples.