Accuracy in a classification context is defined as (N correct classifications / total classifications). So classifying everything as cancer would, in a balanced dataset, give you ~50% accuracy.

This article is indeed badly written PR fluff. I linked the paper in a sister comment. Both the confusion matrix and the ROC curve look phenomenal. Train/test/validation split seems fine too, as do the training diagnostics, so I'm optimistic that it isn't a case of overfitting.

Ofcourse 3rd party replication would be welcome, and I can't speak to the medical relevanve of the dataset. But the computer vision side of things seems well executed.