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| Active Iterative Optimization of Leukocyte Image Classification Model with Fused Attention |
| Jiang Shuying, Li Zhiming, Mo Xian, Sun Ang, Zhang Junran#* |
| (College of Electrical Engineering, Sichuan University, Chengdu 610065, China) |
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Abstract The number and structural characteristics of leukocytes contain important information about human health, and the counting of different types of leukocytes can provide important basis for the early treatment of many diseases. However, the high cost of collecting and labeling leukocyte data integration and the small number of available leukocyte datasets currently pose challenges for automatic computer-aided leukocyte classification. To address these challenges, an active iterative optimization leukocyte image classification model incorporating attention was proposed in this paper. By attaching a LossNet network for active learning to the ResNet18 backbone network, the most representative samples were selected from a large number of unlabeled samples for labeling, reducing the amount of samples that need to be manually labeled. Meanwhile, to cope with the impact of inter-class imbalance in the leukocyte dataset on active learning, this paper added an active iterative augmentation module to select difficult samples in the training process for data augmentation containing random factors, which formed a two-way information interaction from the bottom up and enhanced the adaptability of the model to imbalanced datasets. Finally, after comparing three attention modules, this paper chose to incorporate the CBAM attention module to enhance the model′s focus on the leukocyte feature regions and improve the model′s performance. In this study, the Raabin-WBC dataset containing 14514 leukocyte microscopy images was used for method validation, and the experimental results showed that the classification accuracy of the model proposed in this paper reached 92.35%, 93.64%, and 94.86% when using 28%, 37%, and 52% samples of the training set, respectively, which was 5.14%, 9.24%, and 2.37% higher than the original ResNet18, respectively, and the model greatly reduced the labeling cost of leukocyte dataset, showing wide application prospectives in medical datasets that was lack of labelling.
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Received: 23 February 2023
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Corresponding Authors:
*E-mail: zhangjunran@126.com
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| About author:: #Member, Chinese Society of Biomedical Engineering |
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