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Inspired by the first edition of the contest for HEp-2 cells classification, increasing researches aroused for improving the performance of HEp-2 cells classification.
'Related work' section introduces some related publications on HEp-2 cells classification.
In this paper, we present an automated system for HEp-2 cells classification.
We study the influence of these parameters for HEp-2 cells classification in the 'Discussion' section.
In this section, we verify the effectiveness and improvement of our proposed LLDC framework for HEp-2 cells classification.
These properties are advantageous in HEp-2 cells classification as cell images are unaligned and have high within-class variabilities.
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The discovery led to the development of a cell classification system that is widely used in diagnosing cancer and other illnesses because every type of cell was found to have a unique surface marker.
It seems to be suitable for the HEp-2 cell classification task.
Strong illumination variation is a key challenge in the Human Epithelial Type 2 (HEp-2) cell classification task.
HEp-2 cell classification was performed using Step-Wise LineandDiscriminant Analysis (SWLDA) and Gaussian Mixture Model (GMM).
Blood samples were collected for cell classification and counting at 0, 1, and 2 h during extracorporeal circulation.
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