  Mobile cellular network operators spend nearly a quarter of their revenue on
network maintenance and management. A significant portion of that budget is
spent on resolving faults diagnosed in the system that disrupt or degrade
cellular services. Historically, the operations to detect, diagnose and resolve
issues were carried out by human experts. However, with diversifying cell
types, increased complexity and growing cell density, this methodology is
becoming less viable, both technically and financially. To cope with this
problem, in recent years, research on self-healing solutions has gained
significant momentum. One of the most desirable features of the self-healing
paradigm is automated fault diagnosis. While several fault detection and
diagnosis machine learning models have been proposed recently, these schemes
have one common tenancy of relying on human expert contribution for fault
diagnosis and prediction in one way or another. In this paper, we propose an
AI-based fault diagnosis solution that offers a key step towards a completely
automated self-healing system without requiring human expert input. The
proposed solution leverages Random Forests classifier, Convolutional Neural
Network and neuromorphic based deep learning model which uses RSRP map images
of faults generated. We compare the performance of the proposed solution
against state-of-the-art solution in literature that mostly use Naive Bayes
models, while considering seven different fault types. Results show that
neuromorphic computing model achieves high classification accuracy as compared
to the other models even with relatively small training data
