  Deep learning has achieved great success as a powerful classification tool
and also made great progress in sematic segmentation. As a result, many
researchers also believe that deep learning is the most powerful tool for pixel
level image segmentation. Could deep learning achieve the same pixel level
accuracy as traditional image segmentation techniques by mapping the features
of the object into a non-linear function? This paper gives a short survey of
the accuracies achieved by deep learning so far in image classification and
image segmentation. Compared to the high accuracies achieved by deep learning
in classifying limited categories in international vision challenges, the image
segmentation accuracies achieved by deep learning in the same challenges are
only about eighty percent. On the contrary, the image segmentation accuracies
achieved in international biomedical challenges are close to ninty five
percent. Why the difference is so big? Since the accuracies of the competitors
methods are only evaluated based on their submitted results instead of
reproducing the results by submitting the source codes or the software, are the
achieved accuracies verifiable or overhyped? We are going to find it out by
analyzing the working principle of deep learning. Finally, we compared the
accuracies of state of the art deep learning methods with a threshold selection
method quantitatively. Experimental results showed that the threshold selection
method could achieve significantly higher accuracy than deep learning methods
in image segmentation.
