Relating ROC curves with class statistics
I don't want to offend you in any way but the problem you mentioned above is not really a problem but an opinion that you presented on ROC vs F1 score, and I guess you want other people's opinion on the same.
I would highly suggest you to ask to the point questions next time since if the questions get downvoted you can get banned (I am not downvoting so don't worry), just trying to be informative.
Now from what I was able to grasp from your post, I think that you are having difficulty in choosing the right metric for evaluating the three neural net models you have shown results for.
AUC vs F1 :
In general, the ROC is for many different levels of thresholds and thus it has many F score values. F1 score is applicable for any particular point on the ROC curve.
You may think of it as a measure of precision and recall at a particular threshold value whereas AUC is the area under the ROC curve. For F score to be high, both precision and recall should be high.
Consequently, when you have a data imbalance between positive and negative samples, you should always use F1-score because ROC averages over all possible thresholds.
Conclusion :
So in your case particularly since I can clearly see through precision and recall values that your classes are highly imbalanced, use F1 score to choose the model instead of AUC-ROC score.