This ongoing work outlines a computer vision and deep learning-based pipeline to identify and detect brain biomarkers of diagnostic potential from magnetic resonance imaging (MRI) scans. In this context, this paper describes and analyses two strategies for brain landmark detection, which is a key step in brain biomarker identification: one based on a single Deep Convolutional Neural Network (DCNN) that detects multiple landmarks, and the other based on an ensemble of DCNNs trained to detect one landmark each. Based on our evaluation using two distinct datasets, our preliminary findings demonstrate that the ensemble of DC-NNs achieves superior accuracy in landmarking. Specifically, it successfully detects 84% of the landmarks within a 3mm proximity to their actual locations, with an average error of less than 2mm. In contrast, a single DCNN exhibits an average error of approximately 3mm and locates only 59% of the landmarks within a 3mm distance from their true positions.