Fault diagnosis is turning out to be an intense challenge due to the increasing complexity of the emerging cellular networks. The root-cause analysis of coverage-related network anomalies is traditionally carried out by human experts. However, due to the vast complexity and the increasing cell density of the emerging cellular networks, it is neither practical nor financially viable. To address this, many studies are proposing artificial intelligence (AI)-based solutions using minimization of drive test (MDT) reports. Nowadays, the focus of existing studies is either on diagnosing faults in a single base station (BS) only or diagnosing a single fault in multiple BS scenarios. Moreover, they do not take into account training data sparsity (varying user equipment (UE) densities). Inspired by the emergence of convolutional neural networks (CNN), in this paper, we propose a framework combining CNN and image inpainting techniques for root-cause analysis of multiple faults in multiple base stations in the network that is robust to the sparse MDT reports, BS locations and types of faults. The results demonstrate that the proposed solution outperforms several other machine learning models on highly sparse UE density training data, which makes it a robust and scalable solution for self-healing in a real cellular network.