For image classification, CNN's are still the way to go. But creating your own architecture and training your own novel model aren't necessary for most problems anymore it seems because of transfer learning.
Granted if I tuned both perfectly, CNNs probably would have outperformed but with defaults and a small amount of parameter search, boosting worked best.
Which, BTW, makes them more appealing to me personally. In many real-life cases extra few percent of accuracy matters very little, but ability to just apply something to a problem without much fuss matters a lot.