A Study of the Classification Method of Bone Marrow Blood Cells Based on MobileVit
Keywords:
classification of bone marrow blood cells, morphological diagnosis, Deep learning，MobileVit, classification accuracyAbstract
The automatic classification of bone marrow blood cells is of great significance to diagnosis of acute leukemia. We applied the MobileVit network model to morphological classification of bone marrow cells by using transformer based on self-attention mechanism as convolution for global representation and achieve a global representation with reduced calculation parameters. We found that in the bone marrow blood cell classification task, without the use of transfer learning, MobileVit classification achieved 97% accuracy, exceeding the classification accuracy of Resnet and Densenet networks, and reducing FLOPS and params significantly. Compared to other lightweight networks, the MobileVit model demonstrates improved accuracy and comparable FLOPS and params.
References
Yi Yunlin. “Quality management and clinical significance analysis of bone marrow cell morphology test”. China Contemporary Medicine,2011(29):100-101.
Gu Guiying, Liu Jingxin, Shi Zhangzhen, et al. “Application of computer image recognition in the morphological diagnosis of leukemia”. China Medical Devices, 2018, 33(6): 6-11.
Mendelsohn M L, Mayall B H, Prewitt JMS, et al. “Digital transformation and computer analysis of microscopic image In: Barer Red. advance in Optical and Electron Microscopy” . NewYork: Academic Press, 1968: 245~256
Fu X, Fu M, Li Q, et al. “Morphogo: an automatic bone marrow cell classification system on digital images analyzed by artificial intelligence”. Acta Cytologica, 2020, 64(6): 588-596.
Tian Juanxiu, Liu Guocai, Gu Shanshan, et al. “Research and challenges of deep learning methods for medical image analysis”. Journal of Automation, 2018, 44(3): 401-424.
Zhou M, Wu K, Yu L, et al. “Development and Evaluation of a Leukemia Diagnosis System Using Deep Learning in Real Clinical Scenarios”. Frontiers in Pediatrics, 2021, 9.
Zhang HTA, Liu JX, Zhao XQ, et al. “Artificial intelligence-assisted diagnostic classification of blood cell microscopic images for acute lymphoblastic leukemia based on VGG16”. China Medical Devices, 2019, 34(7): 1-4.
Li Fucui. “Research on morphological diagnosis of leukemia cells based on convolutional neural network combined with migration learning”. Jinan University, 2019.
Dosovitskiy A, Beyer L, Kolesnikov A, et al. “An image is worth 16x16 words: Transformers for image recognition at scale”. arXiv preprint arXiv:2010.11929, 2020.
Mehta S, Rastegari M. “Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer”. arXiv preprint arXiv:2110.02178, 2021.
Sandler M, Howard A, Zhu M, et al. “Mobilenetv2: Inverted residuals and linear bottlenecks”//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4510-4520.
Rasha Salim. “A Single-cell Morphological Dataset of Leukocytes”Internet:www.kaggle.com/rashasalim/blood-smear-images-for-aml-diagnosis?select=AML-Cytomorphology_LMU,2020[May.2021].
Bennett J M, Catovsky D, Daniel M T, et al. “Proposals for the classification of the acute leukaemias French-American-British (FAB) co-operative group”. British journal of haematology, 1976, 33(4): 451-458.
MIT.“Imbalanced-dataset-sampler”Internet:www.github.com/ufoym/imbalanced-dataset-sampler, 2020[2021].
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 American Academic Scientific Research Journal for Engineering, Technology, and Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.
