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Open AccessArticle

A Dynamic Learning Method for the Classification of the HEp-2 Cell Images

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Department of IT Convergence and Application Engineering, Pukyong National University, Busan 48513, Korea
2
Department of Information Security, Tongmyong University, Busan 48520, Korea
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Department of Computer Software Engineering, Dongeui University, Busan 47340, Korea
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(8), 850; https://doi.org/10.3390/electronics8080850
Received: 28 June 2019 / Revised: 20 July 2019 / Accepted: 29 July 2019 / Published: 31 July 2019
(This article belongs to the Section Artificial Intelligence)
The complete analysis of the images representing the human epithelial cells of type 2, commonly referred to as HEp-2 cells, is one of the most important tasks in the diagnosis procedure of various autoimmune diseases. The problem of the automatic classification of these images has been widely discussed since the unfolding of deep learning-based methods. Certain datasets of the HEp-2 cell images exhibit an extreme complexity due to their significant heterogeneity. We propose in this work a method that tackles specifically the problem related to this disparity. A dynamic learning process is conducted with different networks taking different input variations in parallel. In order to emphasize the localized changes in intensity, the discrete wavelet transform is used to produce different versions of the input image. The approximation and detail coefficients are fed to four different deep networks in a parallel learning paradigm in order to efficiently homogenize the features extracted from the images that have different intensity levels. The feature maps from these different networks are then concatenated and passed to the classification layers to produce the final type of the cellular image. The proposed method was tested on a public dataset that comprises images from two intensity levels. The significant heterogeneity of this dataset limits the discrimination results of some of the state-of-the-art deep learning-based methods. We have conducted a comparative study with these methods in order to demonstrate how the dynamic learning proposed in this work manages to significantly minimize this heterogeneity related problem, thus boosting the discrimination results. View Full-Text
Keywords: HEp-2 cell classification; HEp-2; deep learning; convolutional neural networks; discrete wavelet transform; artificial neural network; pattern recognition HEp-2 cell classification; HEp-2; deep learning; convolutional neural networks; discrete wavelet transform; artificial neural network; pattern recognition
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Vununu, C.; Lee, S.-H.; Kwon, O.-J.; Kwon, K.-R. A Dynamic Learning Method for the Classification of the HEp-2 Cell Images. Electronics 2019, 8, 850.

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