Human epithelial-2 cell image classification using deep unsupervised learning and gradient boosting trees
Xu Wang, Tanqiu Jiang, Hengxing Cai · 2021
In this paper, an ensemble learning framework is proposed for HEp-2 cell images, aiming to making use of both handcrafted features and deep learning-based methods. Firstly, deep unsupervised learning is employed to extract features. Then, a gradient boosting trees-based classifier is trained using both handcrafted features and deep learning-based features. Extensive experiments are conducted on benchmark datasets to test the efficiency and robustness of the proposed framework. Experiment results demonstrate hat the proposed framework yield excellent performances compared with existing deep learning-based models.