augmentedImageDatastore increases the training data by performing resizing, rotation, and reflection etc. Technically it is transforming the existing images but for the network it will be a different data and it prevents the network from overfitting and memorizing the exact details of the training images. But in your case, I can see the available images are very less and hence even with data argumentation I belive you are still running short of data.
In case you are not tried, you can also try transfer learning .
