A Deep-learning based Method for the Classification of the Cellular Images

Caleb Vununu, Suk-Hwan Lee, Ki-Ryong Kwon


The present work proposes a classification method for the Human Epithelial of type 2 (HEp-2) cell images using an unsupervised deep feature learning method. Unlike most of the state-of-the-art methods in the literature that utilize deep learning in a strictly supervised way, we propose here the use of the deep convolutional autoencoder (DCAE) as the principal feature extractor for classifying the different types of the HEp-2 cellular images. The network takes the original cellular images as the inputs and learns how to reconstruct them through an encoding-decoding process in order to capture the features related to the global shape of the cells. A final feature vector is constructed by using the latent representations extracted from the DCAE, giving a highly discriminative feature representation. The created features will then be fed to a nonlinear classifier whose output will represent the final type of the cell image. We have tested the discriminability of the proposed features on one of the most popular HEp-2 cell classification datasets, the SNPHEp-2 dataset and the results show that the proposed features manage to capture the distinctive characteristics of the different cell types while performing at least as well as some of the actual deep learning based state-of-the-art methods.


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