The goal of deep anomaly detection is to identify abnormal data by utilizing a deep neural network trained by a normal training dataset. Real-world industrial anomaly detection problems generally distinguish normal and abnormal data through small morphological differences, such as crack and stain. Nevertheless, most existing algorithms focused on capturing not morphological features but semantic features of normal data. Therefore, they suffer poor performance on real-world industrial anomaly detection problems, even though they show their superiority on simulations with representative image classification datasets. To solve this problem, we propose a novel deep anomaly detection method that encourages understanding salient morphological features of normal data. The main idea behind our algorithm is to train a multi-class model to classify between dozens of morphological transformations applied to all the given data. To this end, the proposed algorithm utilizes a self-supervised learning strategy, which makes unsupervised learning straightforwardly. Additionally, we present a kernel size loss to enhance the proposed neural networks' morphological feature representation power. This objective function is defined as the loss between predicted kernel size and label kernel size via morphological transformed images with the label kernel. In all experiments on the industrial dataset, the proposed method demonstrates superior performance. For instance, in the MVTec anomaly detection task, our model achieves the AUROC of 72.92% that is 8.74% higher than the semantic feature-based deep anomaly detection.