Adding dense layer to CNN causes to stop learning
Having dropout after convolution is rather unusual. Try to put it between the dense layers.

Explanation:

Dropout forces neuron to work with a random subset of the preceding neurons' activations. By doing so it breaks the co-adaptation of neurons and improves the generalization of the network. However, for convolutional layer co-adaptation is not an issue, as neighboring pixels of a feature map share their weights and guarantee to have different activations given different inputs in their receptive fields. It may only occur if two layers will learn the same kernel weights, but even then the problem is not so significant. Little number of weights in a convolution layer is a pledge of good generalization.

Here is a short article showing what happens when you use batchnorn/dropout between convolutions: Don’t Use Dropout in Convolutional Networks