Abstract
Blood testing has always been one of the important methods for disease diagnosis, but currently, blood testing instruments face the problems of long time consumption, complex processes, and limited detection types. Therefore, a blood cell dataset for artificial intelligence is necessary. Peripheral Blood Cell Image Dataset (PBCI-DS) provides a total of 17092 images of 8 categories of peripheral blood cells and corresponding cell labeling files. The purpose of PBCI-DS is to serve as a training model for object detection. The experiment uses four YOLO series models (YOLO-v5s, YOLO-v5l, YOLO-v6, YOLO-v7) and SSD models of deep learning methods to train the database, and compares and evaluates the results to prove the effectiveness and usability of PBCI-DS.
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References
Acevedo, A., Alférez, S., Merino, A., Puigvĺ, L., Rodellar, J.: Recognition of peripheral blood cell images using convolutional neural networks. Comput. Methods Programs Biomed. 180, 105020 (2019)
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 Brief 30 (2020)
Alam, M.M., Islam, M.T.: Machine learning approach of automatic identification and counting of blood cells. Healthc. Technol. Lett. 6(4), 103–108 (2019)
Alférez, S., Merino, A., Bigorra, L., Mujica, L., Ruiz, M., Rodellar, J.: Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis. Am. J. Clin. Pathol. 143(2), 168–176 (2015)
Alomari, Y.M., Sheikh Abdullah, S.N.H., Zaharatul Azma, R., Omar, K.: Automatic detection and quantification of WBCS and RBCS using iterative structured circle detection algorithm. Comput. Math. Methods Med. 2014 (2014)
Barger, A.M.: The complete blood cell count: a powerful diagnostic tool. Vet. Clin. Small Anim. Pract. 33, 1207–1222 (2003)
Fujimoto, H., Sakata, T., Hamaguchi, Y., Shiga, S., Tohyama, K.: Flow cytometric method for enumeration and classification of reactive immature granulocyte populations. Cytometry 42(6), 371–378 (2000)
Geissmann, F., Manz, M.G., Jung, S., Sieweke, M.H., Merad, M., Ley, K.: Development of monocytes, macrophages, and dendritic cells. Science 327(5966), 656–661 (2010)
Kaplan, Z.S., Jackson, S.P.: The role of platelets in atherothrombosis. Hematology 2011(1), 51–61 (2011)
Kratz, A., et al.: Digital morphology analyzers in hematology: ICSH review and recommendations. Int. J. Lab. Hematol. 41(4), 437–447 (2019)
LeBien, T.W., Tedder, T.F.: B lymphocytes: how they develop and function. Blood 112(5), 1570–1580 (2008)
Lennon, A.M., Buchanan, A.H., Kinde, I., Warren, A., Honushefsky, A.: Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention. Science 369(6499), eabb9601 (2020)
Li, R., Xiao, X., Ni, S., Zheng, H., Xia, S.: Byte segment neural network for network traffic classification. In: 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), pp. 1–10. IEEE (2018)
Lu, X., Kang, X., Nishide, S., Ren, F.: Object detection based on SSD-ResNet. In: 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 89–92. IEEE (2019)
Marone, G., Lichtenstein, L.M., Galli, S.J.: Mast cells and basophils (2000)
Mayadas, T.N., Cullere, X., Lowell, C.A.: The multifaceted functions of neutrophils. Annu. Rev. Pathol. 9, 181–218 (2014)
Montani, F., Marzi, M.J., Dezi, F., Dama, E., Carletti, R.M.: miR-Test: a blood test for lung cancer early detection. JNCI J. Natl. Cancer Inst. 107(6), djv063 (2015)
Newsome, P.N., Cramb, R., Davison, S.M., Dillon, J.F., Foulerton, M.: Guidelines on the management of abnormal liver blood tests. Gut 67(1), 6–19 (2018)
Olorunshola, O.E., Irhebhude, M.E., Evwiekpaefe, A.E.: A comparative study of YOLOv5 and YOLOv7 object detection algorithms. J. Comput. Soc. Inform. 2(1), 1–12 (2023)
Piaton, E., Fabre, M., Goubin-Versini, I., Bretz-Grenier, M.F., Courtade-Saïdi, M.: Recommandations techniques et règles de bonne pratique pour la coloration de may-grünwald-giemsa: revue de la littérature et apport de l’assurance qualité. In: Annales de Pathologie, vol. 35, pp. 294–305. Elsevier (2015)
Shuo, Y., Hui, L., Fangjun, G., Yanqi, Y., Chenyang, L.: Real-time detection algorithm of mask wearing based on YOLOv5 in complex scenes. Comput. Meas. Control 29(12), 188–194 (2021)
Tordjman, R., Delaire, S., Plouët, J., Ting, S., Gaulard, P., Fichelson, S.: Erythroblasts are a source of angiogenic factors. Blood 97(7), 1968–1974 (2001)
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475 (2023)
Wei, J., Qu, Y.: Lightweight improvement of YOLOv6 algorithm for small target detection (2023)
Acknowledgements
We thank B.A. Qiuqi from Foreign Studies College of Northeastern University, China, for her professional English proofreading in this paper. We thank Miss Zixian Li and Mr. Guoxian Li for their important discussion. This work is support by “National Natural Science Foundation of China" (No. 82220108007). PBCI-DS is open available at: https://figshare.com/articles/figure/PBCI-DS_A_Benchmark_Peripheral_Blood_Cell_Image_Dataset_for_Object_Detection/24417049.
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You, S. et al. (2023). PBCI-DS: A Benchmark Peripheral Blood Cell Image Dataset for Object Detection. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14180. Springer, Cham. https://doi.org/10.1007/978-3-031-46677-9_35
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DOI: https://doi.org/10.1007/978-3-031-46677-9_35
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