Classification of microscopic peripheral blood cell images using multibranch lightweight CNN-based model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09158-9.pdf
Reference77 articles.
1. Girdhar A, Kapur H, Kumar V (2022) Classification of white blood cell using convolution neural network. Biomed Signal Process Control 71:103156. https://doi.org/10.1016/j.bspc.2021.103156
2. Balasubramanian K, Ananthamoorthy NP, Ramya K (2022) An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm. Neural Comput Appl 34:16089–16101. https://doi.org/10.1007/s00521-022-07279-1
3. Hegde RB, Prasad K, Hebbar H (2019) Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images. Integr Med Res 39:382–392. https://doi.org/10.1016/j.bbe.2019.01.005
4. Dhal KG, Rai R, Das A et al (2023) Chaotic fitness-dependent quasi-reflected Aquila optimizer for superpixel based white blood cell segmentation. Neural Comput Appl. https://doi.org/10.1007/s00521-023-08486-0
5. Agustin RI, Arif A, Sukorini U (2021) Classification of immature white blood cells in acute lymphoblastic leukemia L1 using neural networks particle swarm optimization. Neural Comput Appl 33:10869–10880. https://doi.org/10.1007/s00521-021-06245-7
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