Classification of Blood Cells with Convolutional Neural Network Model

Yıl 2024, Cilt: 13 Sayı: 1, 314 - 326, 24.03.2024

Emrah Aslan Yıldırım Özüpak

https://doi.org/10.17798/bitlisfen.1401294

Cited By: 1

Öz

Among the blood cells, white blood cells (WBC), which play a crucial role in forming our body's defense system, are essential components. Originating in the bone marrow, these cells serve as the fundamental components of the immune system, shouldering the responsibility of safeguarding the body against foreign microbes and diseases. Insufficient WBC counts may compromise the body's skill to resist infections, a status known as leukopenia. White blood cell counting is a specialty procedure that is usually carried out by qualified physicians and radiologists. Thanks to recent advances, image processing techniques are frequently used in biological systems to identify a wide spectrum of illnesses. In this work, image processing techniques were applied to enhance the white blood cell deep learning models' classification accuracy. To expedite the classification process, Convolutional Neural Network (CNN) models were combined with Ridge feature selection and Maximal Information Coefficient techniques. These tactics successfully determined the most important characteristics. The selected feature set was then applied to the classification procedure. ResNet-50, VGG19, and our suggested model were used as feature extractors in this study. The categorizing of white blood cells was completed with an amazing 98.27% success rate. Results from the experiments demonstrated a considerable improvement in classification accuracy using the proposed CNN model.

Anahtar Kelimeler

CNN, White Blood Cell, Deep Learning, Artificial Intelligent

Kaynakça

• [1] G.C. Kabat, M.Y. Kim, J.A.E. Manson, L. Lessin, J. Lin, S. Wassertheil-Smoller, T.E. Rohan, "White blood cell count and total and cause-specific mortality in the women’s health initiative," Am. J. Epidemiol., vol. 186, pp. 63–72, 2017. (http://dx.doi.org/10.1093/aje/kww226)
• [2] A. Mbanefo and N. Kumar, "Evaluation of malaria diagnostic methods as a key for successful control and elimination programs," Trop Med Infect Dis, vol. 5, no. 2, p. 102, 2020.
• [3] S. Nema, M. Rahi, A. Sharma, and P.K. Bharti, "Strengthening malaria microscopy using artificial intelligence-based approaches in India," Lancet Reg Health - Southeast Asia, vol. 5, p. 100054, 2022.
• [4] World Health Organization, Malaria microscopy quality assurance manual-version 2, 2021.
• [5] K.A.L.-D.ulaimi, I. Tomeo-Reyes, J. Banks, and V. Chandran, "Evaluation and benchmarking of level set-based three forces via geometric active contours for segmentation of white blood cell nuclei shape," Comput. Biol. Med., vol. 116, p. 103568, 2020. [doi:10.1016/j.compbiomed.2019.103568] (http://dx.doi.org/10.1016/j.compbiomed.2019.103568)
• [6] J. Zhao, M. Zhang, Z. Zhou, J. Chu, and F. Cao, "Automatic detection and classification of leukocytes using convolutional neural networks," Med. Biol. Eng. Comput., vol. 55, pp. 1287–1301, 2017. [doi:10.1007/s11517-016-1590-x](http://dx.doi.org/10.1007/s11517-016-1590-x)
• [7] P. Chun, I. Ujike, K. Mishima, M. Kusumoto, and S. Okazaki, "Random forest-based evaluation technique for internal damage in reinforced concrete featuring multiple nondestructive testing results," Constr. Build. Mater., vol. 253, p. 119238, 2020. [doi:10.1016/j.conbuildmat.2020.119238](http://dx.doi.org/10.1016/j.conbuildmat.2020.119238)
• [8] A. Barai, M.F. Faruk, S.M. Shuvo, A.Y. Srizon, S.M. Hasan, and A. Sayeed, "A Late Fusion Deep CNN Model for the Classification of Brain Tumors from Multi-Parametric MRI Images," in 2023 International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM), Gazipur, Bangladesh, 2023, pp:1-6. https://doi.org/10.1109/NCIM59001.2023.10212729).
• [9] N. Mahajan and H. Chavan, "MRI Images Based Brain Tumor Detection Using CNN for Multiclass Classification," in 2023 3rd Asian Conference on Innovation in Technology (ASIANCON), Ravet IN, India, 2023, pp. 1-5.(https://doi.org/10.1109/ASIANCON58793.2023.10270492)
• [10] K. Kaplan, Y. Kaya, M. Kuncan, M.R. Minaz, and H.M. Ertunç, "An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis," Appl. Soft Comput., vol. 87, p. 106019, 2020. [doi:10.1016/j.asoc.2019.106019](http://dx.doi.org/10.1016/j.asoc.2019.106019)
• [11] R. Singh, A. Sharma, N. Sharma, and R. Gupta, "Impact of Adam, Adadelta, SGD on CNN for White Blood Cell Classification," in 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2023, pp. 1702-1709. [doi:10.1109/ICSSIT55814.2023.10061068](http://dx.doi.org/10.1109/ICSSIT55814.2023.10061068)
• [12] S. Montaha, S. Azam, A. Rafid, M. Hasan, Z. Karim, and A. Islam, "TimeDistributed-CNNLSTM: A Hybrid Approach Combining CNN and LSTM to Classify Brain Tumor on 3D MRI Scans Performing Ablation Study," IEEE Access, vol. 10, pp. 60039-60059, 2022. [doi:10.1109/ACCESS.2022.3179577](https://doi.org/10.1109/ACCESS.2022.3179577)
• [13] S. Saeedi, S. Rezayi, H. Keshavarz, and S. R Niakan Kalhori, "MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques," BMC Med Inform Decis Mak, vol. 23, no. 1, p. 16, Jan. 23, 2023. [doi:10.1186/s12911-023-02114-6](https://doi.org/10.1186/s12911-023-02114-6)
• [14] I.M. Baltruschat, H. Nickisch, M. Grass, T. Knopp, and A. Saalbach, "Comparison of deep learning approaches for multi-label chest X-ray classification," Sci. Rep., vol. 9, p. 6381, 2019. [doi:10.1038/s41598-019-42294-8](http://dx.doi.org/10.1038/s41598-019-42294-8)
• [15] H.P. Beck, "Digital microscopy and artificial intelligence could profoundly contribute to malaria diagnosis in elimination settings," Front Artif Intell, vol. 5, p. 510483, 2022.
• [16] Y. Kumar, A. Koul, and S. Mahajan, "A deep learning approaches and fastai text classification to predict 25 medical diseases from medical speech utterances, transcription and intent," Soft Comput, vol. 26, no. 17, pp. 8253–8272, 2022.
• [17] P.S. Kumar and S. Vasuki, "Automated diagnosis of acute lymphocytic leukemia and acute myeloid leukemia using multi-SV," Journal of Biomedical Imaging and Bioengineering, vol. 1, no. 1, pp. 20–24, 2017.
• [18] S. Nazlibilek, D. Karacor, T. Ercan, M.H. Sazli, O. Kalender, and Y. Ege, "Automatic segmentation, counting, size determination and classification of white blood cells," Measurement, vol. 55, pp. 58–65, 2014.(https://doi.org/10.1016/j.measurement.2014.04.008)
• [19] Y. Li, R. Zhu, L. Mi, Y. Cao and D. Yao, "Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method," Computational and Mathematical Methods in Medicine, pp. 1–12, 2016. [doi:10.1155/2016/9514707]( https://doi.org/10.1155/2016/9514707)
• [20] P, Mooney., Kaggle Dataset, Blood Cell Images, 14 March 2018 [Online] Avaible: https://www.kaggle.com/datasets/paultimothymooney/blood-cells.
• [21] I. Rojas, O. Valenzuela, F. Rojas, ve F. Ortuño, "Bioinformatics and Biomedical Engineering: 7th International Work-Conference. Proceedings 2019; Part I," (11465).
• [22] L. Ma, R. Shuai, X. Ran, W. Liu, ve C. Ye, "Combining DC-GAN with ResNet for blood cell image classification," Medical & biological engineering & computing, vol 58, no. 6, pp. 1251-1264, 2020.
• [23] A. Şengür, Y. Akbulut, Ü. Budak, ve Z. Cömert, "White blood cell classification based on shape and deep features," International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1-4, 2019.
• [24] A. M. Patil, M. D. Patil, ve G. K. Birajdar, "White blood cells image classification using deep learning with canonical correlation analysis," IRBM, vol 42, no. 5, pp. 378-389, 2021.
• [25] A. Çınar ve S. A. Tuncer, "Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM," SN Applied Sciences, vol 3, no. 4, pp. 1-11, 2021.
• [26] A. Girdhar, H. Kapur, ve V. Kumar, "Classification of White blood cell using Convolution Neural Network," Biomedical Signal Processing and Control, vol 71, no. 103156, 2022.
• [27] W. Yu, J. Chang, C. Yang, L. Zhang, H. Shen, Y. Xia, ve J. Sha, "Automatic classification of leukocytes using deep neural network," 12th international conference on ASIC (ASICON), pp. 1041-1044, 2017.
• [28] M. J. Macawile, V. V. Quiñones, A. Ballado, J. D. Cruz, ve M. V. Caya, "White blood cell classification and counting using convolutional neural network," 3rd International conference on control and robotics engineering (ICCRE), pp. 259-263, 2018.
• [29] J. Zhao, M. Zhang, Z. Zhou, J. Chu, ve F. Cao, "Automatic detection and classification of leukocytes using convolutional neural networks," Medical & biological engineering & computing, vol 55, no. 8, pp. 1287-1301, 2017.
• [30] Y. Ming, E. Zhu, M. Wang, Y. Ye, X. Liu, ve J. Yin, "DMP-ELMs: Data and model parallel extreme learning machines for large-scale learning tasks," Neurocomputing, vol 320, pp. 85-97, 2018.
• [31] M. Imran Razzak ve S. Naz, "Microscopic blood smear segmentation and classification using deep contour aware CNN and extreme machine learning," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 49-55, 2017.
• [32] R. B. Hegde, K. Prasad, H. Hebbar, ve B. M. K. Singh, "Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images," Biocybernetics and Biomedical Engineering, vol 39, no. 2, pp. 382-392, 2019.