This occurs when the model incorrectly classifies healthy individuals as having the disease. While this leads to patient anxiety, unnecessary follow-up tests, and increased costs, it is generally considered less harmful than failing to detect an actual disease case (a false negative).

At first, all entries of this matrix are set to zero. Then, each prediction is compared with its true label, and their corresponding entry is incremented by one. Confusion matrix entries are used to calculate counting metrics like TP, FP, FN, and TN.

github.com/GoktugGuverc...

In that case, embedding vectors generated for different images
become constant. This is called dimensional collapse.

Compared with CNNs, transformers are
computationally more expensive and require much more data for successful training. Besides, transformer features do not tend to exhibit distinct properties.