Streamlined Classification of Microscopic Blood Cell Images
Keywords:Deep Learning, Autoimmune diseases, WBC, Convolutional Neural Network
Deep learning is a kind of AI that mimics how humans learn certain things. Unlike traditional machine learning algorithms, deep learning algorithms are piled high with increasing complexity and abstraction. Image classification is a subject of Deep Learning where we may categorize photos into classes based on their attributes. White blood cells are a component of the body. They help the body fight infections and disorders. Neutrophils, lymphocytes, monocytes, and eosinophils are white blood cells. The classification algorithm may be used to classify current data based on coaching knowledge. A software learns from a dataset or collection of observations and then classifies fresh data into classes or groups. This work aims to propose a newly deviced CNN model called LYMPONET which has been employed to differentiate the types of white blood cells, which may be used to predict many autoimmune disorder. LYMPONET is a bespoke CNN-based architecture that performs better than existing CNN models like VGG16, InceptionV3, Xception, and ResNet152V2, as measured by performance measures.
M. Patil, M.D. Patil, G.K. Birajdar, "White Blood Cells Image Classiﬁcation Using Deep Learning with Canonical Correlation Analysis", IRBM Volume 42, Issue 5, October 2021.
Li Ma, Renjun Shuai, Xuming Ran, Wenjia Liu, Chao Ye, "Combining DC-GAN with ResNet for blood cell image classiﬁcation", Medical & Biological Engineering & Computing, March 2020.
Qian Huang, Wei Li, Baochang Zhang, Qingli Li, Ran Tao, Nigel H. Lovell, "Blood Cell Classification Based on Hyperspectral Imaging With Modulated Gabor and CNN", IEEE Journal of Biomedical and Health Informatics, March 2020.
Mesut Togacar, Burhan Ergen, Mehmet Emre Sertkaya, "Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models", Elektronika Ir Elektrotechnika (ISSN 1392-1215), Vol. 25, No. 5, October 2019.
Andrea Acevedo, Santiago Alférez, Anna Merino, Laura Puigví, José Rodellar, "Recognition of peripheral blood cell images using convolutional neural networks", Computer Methods and Programs in Biomedicine, Volume 180, October 2019.
Mayank Sharma, Aishwarya Bhave and Rekh Ram Janghel, "White Blood Cell Classification Using Convolutional Neural Network", Advances in Intelligent Systems and Computing (AISC, volume 900), January 2019.
Ming Jiang, Liu Cheng, Feiwei Qin, Lian Du, Min Zhang, "White Blood Cells Classification with Deep Convolutional Neural Networks", International Journal of Pattern Recognition and Artificial Intelligence, Vol. 32, No. 09, 2018.
Md Zahangir Alom, Chris Yakopcic, Tarek M. Taha, Vijayan K Asari, "Microscopic Blood Cell Classified using Inception Recurrent Residual Convolutional Neural Networks", IEEE National Aerospace and Electronics Conference, July 2018.
Merl James Macawile, Vonn Vincent Quiñones, Alejandro Ballado Jr., Jennifer Dela Cruz, Meo Vincent Caya, "White Blood Cell Classification and Counting Using Convolutional Neural Network", 2018 3rd International Conference on Control and Robotics Engineering, April 2018.
Wei Yu, Jing Chang, Cheng Yang, Limin Zhang, Han Shen, Yongquan Xia, Jin Sha, " This is the Automatic Classification of Leukocytes Using Deep Neural Network", 2017 IEEE 12th International Conference on ASIC (ASICON), October 2017.
Q. Li, Y. Xue, Z. Liu, and X. Yue, "A hyperspectral Picture based tongue surface examination and order calculation" O. Rajadell, P. Garc, 2021.
S.UshaKiruthika, S. Kanaga Suba Raja, V. Balaji, C.J.Raman, S. S. L. Durai Arumugam , Detection of Tuberculosis in Chest X-rays using U-Net Architecture‟, International Journal of Innovative Technology and Exploring Engineering, ISSN: 2278–3075, 2019.
M. Adjouadi, M. Ayala, M. Cabrerizo, N. Zong, G. Lizarraga, and M. Rossman, "Order of leukemia blood tests utilizing neural organizations," in M. Adjouadi, M. Ayala, M. Cabrerizo, N. Zong, G. Lizarraga, and M. Rossman, "Order of leukemia blood tests utilizing neural organizations.
M. Su, C. Cheng, and P. Wang, "A neural-network-based way to deal with white platelet arrangement”, The Scientific World Journal, 2014.
“Gabor-filtering centred closest regularised subspace for hyperspectral image classification”, IEEE Journal of Selected Topics in Applied Earth Measurements and Remote Sensing, vol. 7, no. 4, 2014.
X. Kang, C. Li, S. Li, and H. Lin, “Classification of hyperspectral photographs using a deep network focused on Gabor filtering,” IEEE Journal of Selected Topics of Applied Earth Observations and Remote Sensing, 2017.
M. Adjouadi, M. Ayala, M. Cabrerizo, N. Zong, G. Lizarraga, and M. Rossman, “Classification of leukaemia blood samples using neural networks,” Annals of Biomedical Engineering, vol. 38.
M. Su, C. Cheng, and P. Wang, “A neural-network-based approach to image processing, “The Scientific World Journal, vol. 2014, white blood cell classification, 2014.
M. Su, C. Cheng, and P. Wang, “A neural-network-based approach to white blood cell classification”, The Scientific World Journal, 2014.
Li Y, Zhu R, Mi L, et al.: Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method. Comput. Math. Methods Med, 2016.
Bhavnani LA, Jaliya UK, Joshi MJ: Segmentation and Counting of WBCs and RBCs from Microscopic Blood Sample Images. International Journal of Image, Graphics and Signal Processing, 2016.
Quinones VV, Macawile MJ, Ballado A, et al.: Leukocyte segmentation and counting based on microscopic blood images using HSV saturation component with blob analysis. 3rd International Conference on Control and Robotics Engineering (ICCRE), 2018.
Safuan SN, Tomari MR, Zakaria WN: White blood cell (WBC) counting analysis in blood smear images using various color segmentation methods. Measurement, 2018.
Salem N, Sobhy NM, Dosoky ME: A Comparative Study of White Blood cells Segmentation using Otsu Threshold and Watershed Transformation. Journal of Biomedical Engineering and Medical Imaging, 2016.
Ferdosi BJ, Nowshin S, Sabera FA, Habiba: White Blood Cell Detection and Segmentation from Fluorescent Images with an Improved Algorithm using K-means Clustering and Morphological Operators. 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), 2018.
How to Cite
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.