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A lightweight deep learning model for acute myeloid leukemia-related blast cell identification

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Abstract

Leukemia is a severe blood disorder that poses a threat to the life and well-being of patients. Accurate diagnosis of leukemia typing is crucial for treatment and prognosis. However, manual diagnosis requires significant resources and is subject to observer variation. Computer-aided diagnostic (CAD) technologies have emerged as a promising approach to complement manual diagnosis and alleviate the workload of specialized physicians. With the recent advances in deep learning, the performance of CAD has been substantially enhanced. In this paper, we propose a novel classification model, AMLNet, for acute myeloid leukemia (AML) cells. AMLNet is a lightweight model based on the ShuffleNet V2 architecture. We first validate the performance of the convolutional neural network and Transformer architecture models on the largest AML cell dataset to date. Then, we enhance the data augmentation approach and model structure of the ShuffleNet V2 model to develop AMLNet. The AUC, sensitivity, and accuracy of AMLNet are up to 0.9935%, 63.91%, and 96.43%, respectively, which achieves the optimal classification performance. Finally, the classification performance of AMLNet is verified on other public datasets, which is superior to other methods. AMLNet presents a promising solution for practical diagnosis scenarios since the end devices of assisted diagnosis often have limited computational power. The proposed AMLNet model effectively improves the classification performance of ShuffleNet V2 while offering the advantages of high performance and low computational consumption. Overall, our study demonstrates the potential of lightweight deep learning models in improving the accuracy of leukemia typing and reducing the workload of specialized physicians.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Funding

This research was funded by CAS Engineering Laboratory for In Vitro Diagnostic (Grant No. 20210419115032678), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA16021102), and Suzhou Key Technology (Research) Project of Critical and Infectious Diseases Precaution and Control (Grant No. GWZX202102).

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Authors and Affiliations

  1. School of Biomedical Engineering (Suzhou), Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230026, People’s Republic of China

    Bing Leng, Bidou Wang, Jinxian Wang & Gangyin Luo

  2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, People’s Republic of China

    Bing Leng, Hao Jiang, Bidou Wang, Jinxian Wang & Gangyin Luo

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  1. Bing Leng
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  4. Jinxian Wang
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Contributions

BL was involved in the investigation, formal analysis, and writing—original draft. HJ contributed to writing—review and editing. BW performed the conceptualization. JW assisted in the investigation and supervision. GL contributed to the project administration and funding acquisition.

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Correspondence to Jinxian Wang or Gangyin Luo.

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Leng, B., Jiang, H., Wang, B. et al. A lightweight deep learning model for acute myeloid leukemia-related blast cell identification. J Supercomput 80, 15215–15244 (2024). https://doi.org/10.1007/s11227-024-06063-3

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