Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

Oğuzhan Katar; İlhan Fırat Kılınçer

doi:10.35377/saucis...1196934

EN

Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

Abstract

White blood cells (WBCs), which are part of the immune system, help our body fight infections and other diseases. Certain diseases can cause our body to produce fewer WBCs than it needs. For this reason, WBCs are of great importance in the field of medical imaging. Artificial intelligence-based computer systems can assist experts in the analysis of WBCs. In this study, an approach is proposed for the automatic classification of WBCs over five different classes using a pre-trained model. ResNet-50, VGG-19, and MobileNet-V3-Small pre-trained models were trained with ImageNet weights. In the training, validation, and testing processes of the models, a public dataset containing 16,633 images and not having an even class distribution was used. While the ResNet-50 model reached 98.79% accuracy, the VGG-19 model reached 98.19% accuracy, the MobileNet-V3-Small model reached the highest accuracy rate with 98.86%. When the predictions of the MobileNet-V3-Small model are examined, it is seen that it is not affected by class dominance and can classify even the least sampled class images in the dataset correctly. WBCs were classified with high accuracy using the proposed pre-trained deep learning models. Experts can effectively use the proposed approach in the process of analyzing WBCs.

Keywords

References

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Oğuzhan Katar ^*
0000-0002-5628-3543
Türkiye

İlhan Fırat Kılınçer
0000-0001-8090-4998
Türkiye

Publication Date

December 31, 2022

Submission Date

October 31, 2022

Acceptance Date

December 22, 2022

Published in Issue

Year 2022 Volume: 5 Number: 3

DOI

https://doi.org/10.35377/saucis...1196934

IZ

https://izlik.org/JA39ML49AM

Cite

RIS / Bibtex

APA

Katar, O., & Kılınçer, İ. F. (2022). Automatic Classification of White Blood Cells Using Pre-Trained Deep Models. Sakarya University Journal of Computer and Information Sciences, 5(3), 462-476. https://doi.org/10.35377/saucis...1196934

AMA

1.Katar O, Kılınçer İF. Automatic Classification of White Blood Cells Using Pre-Trained Deep Models. SAUCIS. 2022;5(3):462-476. doi:10.35377/saucis.1196934

Chicago

Katar, Oğuzhan, and İlhan Fırat Kılınçer. 2022. “Automatic Classification of White Blood Cells Using Pre-Trained Deep Models”. Sakarya University Journal of Computer and Information Sciences 5 (3): 462-76. https://doi.org/10.35377/saucis. 1196934.

EndNote

Katar O, Kılınçer İF (December 1, 2022) Automatic Classification of White Blood Cells Using Pre-Trained Deep Models. Sakarya University Journal of Computer and Information Sciences 5 3 462–476.

IEEE

[1]O. Katar and İ. F. Kılınçer, “Automatic Classification of White Blood Cells Using Pre-Trained Deep Models”, SAUCIS, vol. 5, no. 3, pp. 462–476, Dec. 2022, doi: 10.35377/saucis...1196934.

ISNAD

Katar, Oğuzhan - Kılınçer, İlhan Fırat. “Automatic Classification of White Blood Cells Using Pre-Trained Deep Models”. Sakarya University Journal of Computer and Information Sciences 5/3 (December 1, 2022): 462-476. https://doi.org/10.35377/saucis. 1196934.

JAMA

1.Katar O, Kılınçer İF. Automatic Classification of White Blood Cells Using Pre-Trained Deep Models. SAUCIS. 2022;5:462–476.

MLA

Katar, Oğuzhan, and İlhan Fırat Kılınçer. “Automatic Classification of White Blood Cells Using Pre-Trained Deep Models”. Sakarya University Journal of Computer and Information Sciences, vol. 5, no. 3, Dec. 2022, pp. 462-76, doi:10.35377/saucis. 1196934.

Vancouver

1.Oğuzhan Katar, İlhan Fırat Kılınçer. Automatic Classification of White Blood Cells Using Pre-Trained Deep Models. SAUCIS. 2022 Dec. 1;5(3):462-76. doi:10.35377/saucis. 1196934