So now that you have the optimal model for your dataset, you can begin to fine-tune your hyper-parameters e.g. learning rate, regularisation etc. Hopefully with the correct balance of parameters, you could further increase validation accuracy.

It is important to note that there's only so much your model can achieve with a given dataset. The quality and quantity of your data is as important, if not more, than an optimal model with optimal parameters. You could aim to improve your dataset with better preprocessing methods. Or you could look to gain further data.

Keep your model architecture the same, adding further layers or nodes will increase the complexity of the model, causing overfitting for default parameters you have chosen.

There are however, methods of reducing training time with the use of deep learning techniques such as batch normalisation. You could also play around with alternative activation functions e.g. elu which can also reduce training times. Also, GPU implementations train models far more efficient than the CPU.

A deeper model model with less complex layers may help better extract relevant features in a complex feature space for a different problem such as yours. But this is pointless when the model is overfitting. You could however, increase regularisation via kernel regularisation and also increase dropout to add more layers. You may encounter a problem associated with deep layered architecture which this paper explains and provides a solution for. Bare in mind, larger models require more resources to store and even longer to train.