Smartphones and tablets are nowadays targets of malicious writers, that are able to develop more and more aggressive malicious applications to exfiltrate information from mobile devices. The signature-based detection currently exploited in (commercial and free) mobile antimalware is not able to detect never seen threats, as a matter of fact, antimalware are able just to recognise a malware if its signature is stored into the antimalware repository. With this in mind, we propose a mobile malware detector. We consider a dynamic analysis, in particular, we extract system call traces from running applications that, once transformed into images, represent the input for a deep neuro-fuzzy model. The aim of the deep neuro-fuzzy model is to discern malware applications from legitimate ones. We evaluate the deep neuro-fuzzy model effectiveness by considering a dataset composed by 6817 (malware and trusted) real-world Android samples, by reaching a training accuracy of 0.95 and a testing accuracy equal to 0.9, with the aim to empirically demonstrate the effectiveness of the proposed deep neuro-fuzzy model in the Android malware detection task.