Objective: Hydrocephalus is the leading indication for pediatric neurosurgical care worldwide. Identification of postinfectious hydrocephalus (PIH) verses non-postinfectious hydrocephalus, as well as the pathogen involved in PIH is crucial for developing an appropriate treatment plan. Accurate identification requires clinical diagnosis by neuroscientists and microbiological analysis, which are time-consuming and expensive. In this study, we develop a domain enriched AI method for computerized tomography (CT)-based infection diagnosis in hydrocephalic imagery. State-of-the-art (SOTA) convolutional neural network (CNN) approaches form an attractive neural engineering solution for addressing this problem as pathogen-specific features need discovery. Yet black-box deep networks often need unrealistic abundant training data and are not easily interpreted. Approach. In this paper, a novel brain attention regularizer is proposed, which encourages the CNN to put more focus inside brain regions in its feature extraction and decision making. Our approach is then extended to a hybrid 2D/3D network that mines inter-slice information. A new strategy of regularization is also designed for enabling collaboration between 2D and 3D branches. Main results. Our proposed method achieves SOTA results on a CURE Children's Hospital of Uganda dataset with an accuracy of 95.8% in hydrocephalus classification and 84% in pathogen classification. Statistical analysis is performed to demonstrate that our proposed methods obtain significant improvements over the existing SOTA alternatives. Significance. Such attention regularized learning has particularly pronounced benefits in regimes where training data may be limited, thereby enhancing generalizability. To the best of our knowledge, our findings are unique among early efforts in interpretable AI-based models for classification of hydrocephalus and underlying pathogen using CT scans.