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Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation
Author(s) -
Wang Yapin,
Cao Yiping
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13904
Subject(s) - convolutional neural network , artificial intelligence , pattern recognition (psychology) , computer science , resampling , feature (linguistics) , image (mathematics) , contextual image classification , deep learning , artificial neural network , peripheral blood , medicine , immunology , philosophy , linguistics
Purpose Human peripheral blood leukocytes’ classification is important for diagnosing blood diseases. Many microscopic leukocyte image automatic detection methods are proposed. In recent years, convolutional neural networks (CNNs) are applied to microscopic leukocyte image automatic classification. But when a CNN is used for microscopic leukocyte image classification, the dataset’s scarcity and imbalance will lead to low classification accuracy. To improve classification accuracy, a data augmentation method is proposed, and a resampling method is adopted when using a CNN method. Methods First, a deep CNN model for microscopic leukocyte image classification is designed. Then, a new data augmentation method based on feature concentration is proposed to enrich the dataset and overcome the problem of dataset scarcity. To make the CNN model focus on the leukocyte region, many images are generated by putting a segmented leukocyte into images with different microscopic surroundings using an image processing method. Finally, taking the imbalance of the five kinds of leukocytes in the dataset into consideration, a resampling method is adopted. The resampling method iteratively feeds the leukocyte images with a low proportion to the CNN model within an epoch to ensure that images of each of the five kinds of leukocytes are represented in relatively equal numbers in each batch. Results The experimental results demonstrate that the proposed classification method can achieve 97.6% average testing accuracy. Classification precision for the five kinds of leukocytes is above 93.4%, while sensitivity is above 92.5%. Both the proposed data augmentation and the resampling methods improve classification accuracy. Conclusions A human peripheral blood leukocyte classification method based on a CNN and data augmentation is proposed. The problem of dataset scarcity is solved by the proposed data augmentation method, and the dataset imbalance is solved by a resampling method.