In this paper, we propose an ensemble of tree- structured learning architecture to improve the discriminative capability of deep convolutional neural network (DCNN) for image classification. In the proposed technique, the path from the root node to a leaf node represents a classification rule. Thus, to maximize the classification accuracy, each internal node needs to make an optimal binary decision to the left or the right child node. To this aim, we develop a tree-CNN as a randomized tree to embed a DCNN into each internal node and train the model to determine the best traversing path to predict a class. Classification of some images with similar statistical properties yet belonging to different classes are difficult with the conventional DCNN architecture. Thus, to resolve the problem, we use a coarse-to-fine approach where subsequent networks in children nodes are hierarchically and randomly organized to discriminate smaller sets of classes than those in a parent node. The results from all the individual tree-CNNs are ensembled to make the final decision in classification. The proposed technique is implemented with the state-of-the-art deep network model, i.e., Wide Residual Network DCNN model [19], and is demonstrated with experimental results to outperform the classification performance over the anchor.