Accurate Lymph Node detection plays a significant role in tumour staging, choice of therapy, and in predicting the outcome of malignant diseases. Clinical examination to detect lymph node metastases alone is tedious and error-prone due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying shapes, poses, sizes, and sparsely distributed locations. (Oda et al., 2017) report 84.2% sensitivity at 9.1 false-positives per volume (FP/vol.) by local intensity structure analysis based on an Intensity Targeted Radial Structure Tensor (ITRST). In this paper, we first operate a candidate generation stage using U-Net (modified fully convolutional network for segmentation of biomedical images), towards 100% sensitivity at the cost of high FP levels to generate volumes of interest (VOI). Thereafter, we present an exhaustive analysis of approaches using different representations (ways to decompose a 3D VOI) as input to train Convolutional Neural Network (CNN), 3D CNN (convolutional neural network using 3D convolutions) classifier. We also evaluate SVMs trained on features extracted by the aforementioned CNN and 3D CNN. The candidate generation followed by false positive reduction to detect lymph nodes provides an alternative to compute and memory intensive methods using 3D fully convolutional networks. We validate approaches on a dataset of 90 CT volumes with 388 mediastinal lymph nodes published by (Roth et al., 2014). Our best approach achieves 84% sensitivity at 2.88 FP/vol. in the mediastinum of chest CT volumes.