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In the human body, white blood cells (WBCs) are crucial immune cells that help in the early detection of a variety of illnesses. Determination of the number of WBCs can be used to diagnose conditions such as hematological, immunological, and autoimmune diseases, as well as AIDS and leukemia. However, the conventional method of classifying and counting WBCs is time-consuming, laborious, and potentially erroneous. Therefore, this paper presents a computer-assisted automated method for recognizing and detecting WBC categories from blood images. Initially, the blood cell image is preprocessed and then segmented using an effective deep learning architecture called SegNet. Then, the important features are devised and extracted using the EfficientNet architecture. Finally, the WBCs are categorized into four different types using the XGBoost classifier: neutrophils, eosinophils, monocytes, and lymphocytes. The advantages of SegNet, EfficientNet, and XGBoost make the proposed model more robust and achieve a more efficient classification of the WBCs. The BCCD dataset is used to evaluate the performance of the proposed methodology, and the findings are compared to existing state-of-the-art approaches based on accuracy, precision, sensitivity, specificity, and F1-score. Evaluation results show that the proposed approach has a higher rank-1 accuracy of 99.02% and outperformed other existing techniques.
A. Khan, A. Eker, A. Chefranov, et al. White blood cell type identification using multi-layer convolutional features with an extreme-learning machine. Biomedical Signal Processing and Control, 2021, 69: 102932. https://doi.org/10.1016/j.bspc.2021.102932
J. Yao, X. Huang, M. Wei, et al. High-efficiency classification of white blood cells based on object detection. Journal of Healthcare Engineering, 2021, 2021: 1615192. https://doi.org/10.1155/2021/1615192
A. Meenakshi, J. A. Ruth, V. R. Kanagavalli, et al. Automatic classification of white blood cells using deep features based convolutional neural network. Multimedia Tools and Applications, 2022, 81: 30121−30142. https://doi.org/10.1007/s11042-022-12539-2
J. Pfeil, A. Nechyporenko, M. Frohme, et al. Examination of blood samples using deep learning and mobile microscopy. BMC Bioinformatics, 2022, 23(1): 65. https://doi.org/10.1186/s12859-022-04602-4
A. Girdhar, H. Kapur, V. Kumar. Classification of white blood cell using convolution neural network. Biomedical Signal Processing and Control, 2022, 71: 103156. https://doi.org/10.1016/j.bspc.2021.103156
Q. Zhai, B. Fan, B. Zhang, et al. Automatic white blood cell classification based on whole-slide images with a deeply aggregated neural network. Journal of Medical and Biological Engineering, 2022, 42: 126−137. https://doi.org/10.1007/s40846-022-00683-x
E. Başaran. Classification of white blood cells with SVM by selecting SqueezeNet and LIME properties by mRMR method. Signal,Image and Video Processing, 2022, 16: 1821−1829. https://doi.org/10.1007/s11760-022-02141-2
R.A. Bagido, M. Alzahrani, M. Arif. White blood cell types classification using deep learning models. International Journal of Computer Science &Network Security, 2021, 21(9): 223−229.
E. Cengil, A. Çınar, M. Yıldırım. A hybrid approach for efficient multi-classification of white blood cells based on transfer learning techniques and traditional machine learning methods. Concurrency and Computation:Practice and Experience, 2022, 34(6): e6756. https://doi.org/10.1002/cpe.6756
M. Makem, A. Tiedeu, G. Kom, et al. A robust algorithm for white blood cell nuclei segmentation. Multimedia Tools and Applications, 2022, 81(13): 17849−17874. https://doi.org/10.1007/s11042-022-12285-5
Z. Wang, J. Xiao, J. Li, et al. WBC-AMNet: Automatic classification of WBC images using deep feature fusion network based on focalized attention mechanism. PLoS One, 2022, 17(1): e0261848. https://doi.org/10.1371/journal.pone.0261848
D. Ryu, J. Kim, D. Lim, et al. Label-free white blood cell classification using refractive index tomography and deep learning. BME Frontiers, 2021, 2021(1): 18−26.
N. Alofi, W. Alonezi, W. Alawad. WBC-CNN: Efficient CNN-based models to classify white blood cells subtypes. International Journal of Online &Biomedical Engineering, 2021, 17(13): 135−150.
Y. Ha, Z. Du, J. Tian. Fine-grained interactive attention learning for semi-supervised white blood cell classification. Biomedical Signal Processing and Control, 2022, 75: 103611. https://doi.org/10.1016/j.bspc.2022.103611
Y. Afriyie, B. Weyori, A. AOpoku. Classification of blood cells using optimized capsule networks. Neural Processing Letters, 2022, 54: 4809−4828. https://doi.org/10.1007/s11063-022-10833-6
C. Jung, M. Abuhamad, D. Mohaisen, et al. WBC image classification and generative models based on convolutional neural network. BMC Medical Imaging, 2022, 22(1): 94. https://doi.org/10.1186/s12880-022-00818-1
G. Hcini, I. Jdey, A. Heni, et al. Hyperparameter optimization in customized convolutional neural network for blood cells classification. Journal of Theoretical and Applied Information Technology, 2021, 99(22): 5425−5440.
A. Haider, M. Arsalan, Y.W. Lee, et al. Deep features aggregation-based joint segmentation of cytoplasm and nuclei in white blood cells. IEEE Journal of Biomedical and Health Informatics, 2022, 26(8): 3685−3696. https://doi.org/10.1109/JBHI.2022.3178765
X. Yao, K. Sun, X. Bu, et al. Classification of white blood cells using weighted optimized deformable convolutional neural networks. Artificial Cells,Nanomedicine,and Biotechnology, 2021, 49(1): 147−155. https://doi.org/10.1080/21691401.2021.1879823
C. Cheuque, M. Querales, R. León, et al. An efficient multi-level convolutional neural network approach for white blood cells classification. Diagnostics, 2022, 12(2): 248. https://doi.org/10.3390/diagnostics12020248
A. Çınar, S.A. Tuncer. Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM. SN Applied Sciences, 2021, 3: 503. https://doi.org/10.1007/s42452-021-04485-9
Y. Lu, X. Qin, H. Fan, et al. WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet. Applied Soft Computing, 2021, 101: 107006. https://doi.org/10.1016/j.asoc.2020.107006
S. Tarek, H.M. Ebied, A.E. Hassanien, et al. White blood cells segmentation and classification using swarm optimization algorithms and multilayer perceptron. International Journal of Sociotechnology and Knowledge Development, 2021, 13(2): 16−30. https://doi.org/10.4018/IJSKD.2021040102
S. Tavakoli, A. Ghaffari, Z.M. Kouzehkanan, et al. New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images. Scientific Reports, 2021, 11(1): 19428. https://doi.org/10.1007/978-3-031-20650-4_13
A.M. Patil, M.D. Patil, G.K. Birajdar. White blood cells image classification using deep learning with canonical correlation analysis. IRBM, 2021, 42(5): 378−389. https://doi.org/10.1016/j.irbm.2020.08.005
S. Kadry, V. Rajinikanth, D. Taniar, et al. Automated segmentation of leukocyte from hematological images—A study using various CNN schemes. The Journal of Supercomputing, 2022, 78(5): 6974−6994. https://doi.org/10.1007/s11227-021-04125-4
A. Shahzad, M. Raza, J.H. Shah, et al. Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization. Complex &Intelligent Systems, 2021, 8: 3143−3159. https://doi.org/10.1007/s40747-021-00564-x
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