  Transfer learning allows us to exploit knowledge gained from one task to
assist in solving another but relevant task. In modern computer vision
research, the question is which architecture performs better for a given
dataset. In this paper, we compare the performance of 14 pre-trained ImageNet
models on the histopathologic cancer detection dataset, where each model has
been configured as a naive model, feature extractor model, or fine-tuned model.
Densenet161 has been shown to have high precision whilst Resnet101 has a high
recall. A high precision model is suitable to be used when follow-up
examination cost is high, whilst low precision but a high recall/sensitivity
model can be used when the cost of follow-up examination is low. Results also
show that transfer learning helps to converge a model faster.
