Label-free bone marrow white blood cell classification using refractive index tomograms and deep learning

Ryu, Dongryeol; J, Kim; D, Lím; Huang, Min; I, You; D, Cho; Y, Park

doi:10.1101/2020.11.13.381244

Cited by 5 publications

(4 citation statements)

References 60 publications

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“…Therefore, it will not be tested in this paper. In a blood examination of the patient, medical professionals create a slide coated with blood, fix the slide, stain with chemical reagents such as Wright Gimsa and hematoxylin-eosin, and then carefully observe the blood cell changes ( Ryu et al, 2020 ). It takes a long time for doctors to complete a blood cell test.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: BCNet: A Novel Network for Blood Cell Classification

Zhu

Wang

et al. 2022

Front. Cell Dev. Biol.

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Aims: Most blood diseases, such as chronic anemia, leukemia (commonly known as blood cancer), and hematopoietic dysfunction, are caused by environmental pollution, substandard decoration materials, radiation exposure, and long-term use certain drugs. Thus, it is imperative to classify the blood cell images. Most cell classification is based on the manual feature, machine learning classifier or the deep convolution network neural model. However, manual feature extraction is a very tedious process, and the results are usually unsatisfactory. On the other hand, the deep convolution neural network is usually composed of massive layers, and each layer has many parameters. Therefore, each deep convolution neural network needs a lot of time to get the results. Another problem is that medical data sets are relatively small, which may lead to overfitting problems.Methods: To address these problems, we propose seven models for the automatic classification of blood cells: BCARENet, BCR5RENet, BCMV2RENet, BCRRNet, BCRENet, BCRSNet, and BCNet. The BCNet model is the best model among the seven proposed models. The backbone model in our method is selected as the ResNet-18, which is pre-trained on the ImageNet set. To improve the performance of the proposed model, we replace the last four layers of the trained transferred ResNet-18 model with the three randomized neural networks (RNNs), which are RVFL, ELM, and SNN. The final outputs of our BCNet are generated by the ensemble of the predictions from the three randomized neural networks by the majority voting. We use four multi-classification indexes for the evaluation of our model.Results: The accuracy, average precision, average F1-score, and average recall are 96.78, 97.07, 96.78, and 96.77%, respectively.Conclusion: We offer the comparison of our model with state-of-the-art methods. The results of the proposed BCNet model are much better than other state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: BCNet: A Novel Network for Blood Cell Classification

Zhu

Wang

et al. 2022

Front. Cell Dev. Biol.

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“…Furthermore, machine learning approaches can be readily combined with the present platform for rapid classifications of blood cell types and diagnosis of hematologic disorders 62,63 . Finally, other imaging modalities could benefit from our capillary microfluidic device for low-cost, rapid characterization of biological samples 64,65 .…”

Section: Discussionmentioning

confidence: 99%

Reagent- and actuator-free analysis of individual erythrocytes using three-dimensional quantitative phase imaging and capillary microfluidics

Ryu

Nam

Jeon

et al. 2021

Sensors and Actuators B: Chemical

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“…Improved algorithms that benefit from the full 3D information of RI tomograms may resolve the labor-intensive segmentation, augmenting the throughput of our approach. Furthermore, machine learning approaches can be readily combined with the present platform for rapid classifications of blood cell types and diagnosis of hematologic disorders 62, 63 . Finally, other imaging modalities could benefit from our capillary microfluidic device for low-cost, rapid characterization of biological samples 64, 65 .…”

Section: Discussionmentioning

confidence: 99%

Reagent- and actuator-free analysis of individual erythrocytes using three-dimensional quantitative phase imaging and capillary microfluidics

Ryu

Nam

Jeon

et al. 2021

Preprint

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Histopathological examination of blood cells plays a crucial role in the diagnosis of various diseases. However, it involves time-consuming and laborious staining procedures required for microscopic review by medical experts and is not directly applicable for point-of-care diagnosis in resource-limited locations. This study reports a dilution-, actuation- and label-free method for the analysis of individual red blood cells (RBCs) using a capillary microfluidic device and quantitative phase imaging. Blood, without any sample treatment, is directly loaded into a micrometer-thick channel such that it forms a quasi-monolayer inside the channel. The morphological and biochemical properties of RBCs, including hemoglobin concentration, hemoglobin content, and corpuscular volume, were retrieved using the refractive index tomograms of individual RBCs measured using 3D quantitative phase imaging. The deformability of individual RBCs was also obtained by measuring the dynamic membrane fluctuations. The proposed framework applies to other imaging modalities and biomedical applications, facilitating rapid and cost-effective diagnosis and prognosis of diseases.

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Label-free bone marrow white blood cell classification using refractive index tomograms and deep learning

Cited by 5 publications

References 60 publications

RETRACTED: BCNet: A Novel Network for Blood Cell Classification

RETRACTED: BCNet: A Novel Network for Blood Cell Classification

Reagent- and actuator-free analysis of individual erythrocytes using three-dimensional quantitative phase imaging and capillary microfluidics

Reagent- and actuator-free analysis of individual erythrocytes using three-dimensional quantitative phase imaging and capillary microfluidics

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