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A Deep Learning Model for Human Blood Cells Classification

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Advances on Intelligent Computing and Data Science (ICACIn 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 179))

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Abstract

Microscopic imaging is gaining focus in recent days, especially in the part of histopathological image analysis. Blood cells plays a critical role in assessment of health status of patients, especially given the rising frequency of infectious diseases. Automated blood analysis can aid in detecting early stages of diseases. In this work, we present study classification of Blood cell using different transfer learning approaches, MobileNetV2 based model designed for the accurate multi classification of blood cells. Deep Learning (DL) models require more time when training on big data sets, to overcome the computation complexity with light weight model MobileNetV2 is considered. In this paper we present the comparison among different transfer learning models such as VGG16, VGG19, Resnet50 with MobileNetV2. The performance evaluated with Accuracy, Precision, recall and F-Score. MobileNetV2 outperform all other model with accuracy of 97.89%. The proposed model has improved accuracy for classification blood cell for eight class.

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References

  1. Al-masni, M.A., Al-antari, M.A., Choi, M.T., Han, S.M., Kim, T.S.: Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Comput. Methods Programs Biomed. 162, 221–231 (2018). https://doi.org/10.1016/j.cmpb.2018.05.027

    Article  Google Scholar 

  2. Al-antari, M.A., Han, S.M., Kim, T.S.: Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms. Comput. Methods Programs Biomed. 196, 105584 (2020). https://doi.org/10.1016/j.cmpb.2020.105584

    Article  Google Scholar 

  3. Chola, C., Benifa, J.V.B.: Detection and classification of sunspots via deep convolutional neural network. Glob. Transit. Proc., 0–7 (2022). https://doi.org/10.1016/j.gltp.2022.03.006

  4. Al-antari, M.A., Al-masni, M.A., Choi, M.T., Han, S.M., Kim, T.S.: A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int. J. Med. Inform. 117, 44–54 (2018). https://doi.org/10.1016/j.ijmedinf.2018.06.003

    Article  Google Scholar 

  5. Al-masni, M.A., Al-antari, M.A., Min, H., Hyeon, N., Kim, T.: 2nd IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability 2020, ECBIOS 2020, pp. 95–98 (2020)

    Google Scholar 

  6. Al-masni, M.A., Kim, W.R., Kim, E.Y., Noh, Y., Kim, D.H.: Automated detection of cerebral microbleeds in MR images: a two-stage deep learning approach. NeuroImage Clin. 28, 102464 (2020). https://doi.org/10.1016/j.nicl.2020.102464

    Article  Google Scholar 

  7. Li, X., Li, W., Xu, X., Hu, W.: Cell classification using convolutional neural networks in medical hyperspectral imagery, pp. 501–504 (2017)

    Google Scholar 

  8. Chola, C., et al.: Gender identification and classification of Drosophila melanogaster flies using machine learning techniques, vol. 2022 (2022)

    Google Scholar 

  9. Mestetskiy, L.M., Guru, D.S., Benifa, J.V.B., Nagendraswamy, H.S., Chola, C.: Gender identification of Drosophila melanogaster based on morphological analysis of microscopic images. Vis. Comput. (2022). https://doi.org/10.1007/s00371-022-02447-9

  10. Baydilli, Y.Y., Atila, Ü.: Classification of white blood cells using capsule networks. Comput. Med. Imaging Graph. 80, 101699 (2020). https://doi.org/10.1016/J.COMPMEDIMAG.2020.101699

    Article  Google Scholar 

  11. Muaad, A.Y., Hanumanthappa, J., Al-antari, M.A., Bibal Benifa, J.V., Chola, C.: AI-based misogyny detection from Arabic Levantine Twitter tweets. In: Proceedings of the 1st Online Conference on Algorithms, 27 September–October 2021, pp. 4–11. MDPI, Basel, Switzerland (2021). https://doi.org/10.3390/IOCA2021-10880

  12. Muaad, A.Y., Davanagere, H.J., Al-antari, M.A., Benifa, J.V.B., Chola, C. : AI-based misogyny detection from Arabic Levantine Twitter tweets. Comput. Sci. Math. Forum 2(1), 15 (2021)

    Google Scholar 

  13. Muaad, A.Y., et al.: An effective approach for Arabic document classification using machine learning. Glob. Transit. Proc., 0–5 (2022). https://doi.org/10.1016/j.gltp.2022.03.003

  14. Zhao, J., Zhang, M., Zhou, Z., Chu, J., Cao, F.: Automatic detection and classification of leukocytes using convolutional neural networks. Med. Biol. Eng. Comput. 55(8), 1287–1301 (2016). https://doi.org/10.1007/s11517-016-1590-x

    Article  Google Scholar 

  15. Acevedo, A., Alférez, S., Merino, A., Puigví, L., Rodellar, J.: Recognition of peripheral blood cell images using convolutional neural networks. Comput. Methods Programs Biomed. 180, 105020 (2019). https://doi.org/10.1016/j.cmpb.2019.105020

    Article  Google Scholar 

  16. Hung, J., et al.: Applying faster R-CNN for object detection on malaria images, pp. 1–7 (2018). http://arxiv.org/abs/1804.09548

  17. Bani-Hani, D., Khan, N., Alsultan, F., Karanjkar, S., Nagarur, N.: Classification of leucocytes using convolutional neural network optimized through genetic algorithm, November, pp. 1–7 (2018)

    Google Scholar 

  18. Tobias, R.R., et al.: Faster R-CNN model with momentum optimizer for RBC and WBC variants classification. In: LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies, January 2021, pp. 235–239 (2020). https://doi.org/10.1109/LifeTech48969.2020.1570619208

  19. Sajjad, M., et al.: Leukocytes classification and segmentation in microscopic blood smear: a resource-aware healthcare service in smart cities. IEEE Access 5, 3475–3489 (2017). https://doi.org/10.1109/ACCESS.2016.2636218

    Article  Google Scholar 

  20. Long, F., Peng, J., Song, W., **a, X., Sang, J.: Computer methods and programs in biomedicine BloodCaps: a capsule network based model for the multiclassification of human peripheral blood cells, vol. 202 (2021). https://doi.org/10.1016/j.cmpb.2021.105972

  21. Zheng, X., Wang, Y., Wang, G., Liu, J.: Fast and robust segmentation of white blood cell images by self-supervised learning. Micron 107, 55–71 (2018). https://doi.org/10.1016/j.micron.2018.01.010

    Article  Google Scholar 

  22. Acevedo, A., Merino, A., Alférez, S., Molina, Á., Boldú, L., Rodellar, J.: A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data Br. 30, 105474 (2020). https://doi.org/10.1016/j.dib.2020.105474

    Article  Google Scholar 

  23. Wijesinghe, C.B., Wickramarachchi, D.N., Kalupahana, I.N., De Seram, L.R., Silva, I.D., Nanayakkara, N.D.: Fully automated detection and classification of white blood cells. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine & Biology Society EMBS, vol. 2020, pp. 1816–1819 (2020). https://doi.org/10.1109/EMBC44109.2020.9175961

  24. Maity, M., Jaiswal, A., Gantait, K., Chatterjee, J., Mukherjee, A.: Quantification of malaria parasitaemia using trainable semantic segmentation and CapsNet. Pattern Recognit. Lett. 138, 88–94 (2020). https://doi.org/10.1016/j.patrec.2020.07.002

    Article  Google Scholar 

  25. Qin, F., Gao, N., Peng, Y., Wu, Z., Shen, S., Grudtsin, A.: Fine-grained leukocyte classification with deep residual learning for microscopic images. Comput. Methods Programs Biomed. 162, 243–252 (2018). https://doi.org/10.1016/j.cmpb.2018.05.024

    Article  Google Scholar 

  26. Mundhra, D., Cheluvaraju, B., Rampure, J., Rai Dastidar, T.: Analyzing microscopic images of peripheral blood smear using deep learning. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 178–185. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_21

    Chapter  Google Scholar 

  27. Tavakoli, E., Ghaffari, A., Kouzehkanan, Z.M., Hosseini, R.: New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images (2021)

    Google Scholar 

  28. **ang, Q., Zhang, G., Wang, X., Lai, J., Li, R., Hu, Q.: Fruit image classification based on MobileNetV2 with transfer learning technique. In: ACM International Conference Proceeding Series (2019). https://doi.org/10.1145/3331453.3361658

  29. Pramodha, M., Muaad, A.Y., Bibal Benifa, J.V., Hanumanthappa, J., Chola, C., Mugahed, A.: A hybrid deep learning approach for COVID-19 diagnosis via CT and X - R ay medical images, pp. 1–10 (2021)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore, 570006, India

    M. Pramodha, J. Hanumanthappa, Emmanuel Ndagijimana, Abdullah Y. Muaad & Channabasava Chola

  2. Department of Electronics and Communication Engineering, Indian Institute of Information Technology Kottayam, Kottayam, Kerala, India

    S. Ansith & A. A. Bini

  3. Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kottayam, Kerala, India

    J. V. Bibal Benifa & Channabasava Chola

  4. Department of Computer Science, Saba’a Region University, Mareb, Yemen

    Mohammed Al-Sarem

  5. College of Computer Science and Engineering, Taibah University, Medina, 42353, Saudi Arabia

    Mohammed Al-Sarem, Faisal Saeed & Abdulrahman Alqarafi

  6. DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham, B4 7XG, UK

    Faisal Saeed

  7. IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, Guangdong, China

    Md. Belal Bin Heyat

  8. IT Department, Sana’a Community College, Sana’a, 5695, Yemen

    Abdullah Y. Muaad

Authors
  1. M. Pramodha
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  2. S. Ansith
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  3. J. V. Bibal Benifa
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  4. Mohammed Al-Sarem
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  5. J. Hanumanthappa
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  6. A. A. Bini
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  7. Emmanuel Ndagijimana
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  8. Faisal Saeed
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  9. Md. Belal Bin Heyat
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  10. Abdulrahman Alqarafi
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  11. Abdullah Y. Muaad
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  12. Channabasava Chola
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Corresponding authors

Correspondence to Abdullah Y. Muaad or Channabasava Chola .

Editor information

Editors and Affiliations

  1. School of Computing and Digital Technology, Birmingham City University, Birmingham, UK

    Faisal Saeed

  2. Information Systems Department, School of Computing, Universiti Utara Malaysia (UUM), Sintok, Malaysia

    Fathey Mohammed

  3. Faculty of Sciences, University of Hassan II Casablanca, Casablanca, Morocco

    Errais Mohammed

  4. Department of Computing and Technology, Nottingham Trent University, Nottingham, UK

    Tawfik Al-Hadhrami

  5. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Mohammed Al-Sarem

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Pramodha, M. et al. (2023). A Deep Learning Model for Human Blood Cells Classification. In: Saeed, F., Mohammed, F., Mohammed, E., Al-Hadhrami, T., Al-Sarem, M. (eds) Advances on Intelligent Computing and Data Science. ICACIn 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 179. Springer, Cham. https://doi.org/10.1007/978-3-031-36258-3_36

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