Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network

Ferhat Bozkurt

doi:10.53525/jster.1014186

EN TR

Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network

Abstract

Classification of white blood cells plays a significant role in the detection of diseases which are infections caused by abnormalities in the immune system, allergies, anemia, leukemia, cancer, AIDS, etc. In traditional methods, experts manually examine the white blood cells under the microscope, and since this process is tedious, takes more time and can be more error-prone, automated systems have become necessary. Making this classification automatically will help experts in the detection of diseases. In this study, blood cells are classified from blood cell images using dense convolutional neural network. This paper intends to utilize a dense convolutional neural network model to overcome the blood cell classification problem that is one of the most compelling problems in blood diagnosis. In this study, a DenseNet121 model is built to classify blood cell images. Experiments are conducted on an open-access BCCD dataset (12507 white blood cell images that contain four types of white blood cells). Performance evaluations are performed on the accuracy of the techniques, and the results are compared with state-of-the-art deep learning-based approaches as Xception, VGG19, EfficientNetB1. In experimental studies, the highest accuracy (94%) is obtained with the proposed DenseNet121 model. Considering the high accuracy value obtained with this model, automatic detection of which class the cells belong to will speed up the diagnosis and allow more data to be examined by doctors.

Keywords

White blood cells , Classification , Deep learning , DenseNet , Pre-trained model

Dense Evrişimsel Ağ Kullanarak Kan Hücresi Görüntülerinden Kan Hücrelerinin Sınıflandırılması

Öz

Classification of white blood cells plays a significant role in the detection of diseases which are infections caused by abnormalities in the immune system, allergies, anemia, leukemia, cancer, AIDS, etc. In traditional methods, experts manually examine the white blood cells under the microscope, and since this process is tedious, takes more time and can be more error-prone, automated systems have become necessary. Making this classification automatically will help experts in the detection of diseases. In this study, blood cells are classified from blood cell images using dense convolutional neural network. This paper intends to utilize a dense convolutional neural network model to overcome the blood cell classification problem that is one of the most compelling problems in blood diagnosis. In this study, a DenseNet121 model is built to classify blood cell images. Experiments are conducted on an open-access BCCD dataset (12507 white blood cell images that contain four types of white blood cells). Performance evaluations are performed on the accuracy of the techniques, and the results are compared with state-of-the-art deep learning-based approaches as Xception, VGG19, EfficientNetB1. In experimental studies, the highest accuracy (94%) is obtained with the proposed DenseNet121 model. Considering the high accuracy value obtained with this model, automatic detection of which class the cells belong to will speed up the diagnosis and allow more data to be examined by doctors.

Anahtar Kelimeler

White blood cells , Classification , Deep learning , DenseNet , Pre-trained model

References

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Details

Primary Language

English

Subjects

Artificial Intelligence

Journal Section

Research Article

Authors

Ferhat Bozkurt ^*
0000-0003-0088-5825
Türkiye

Publication Date

December 21, 2021

Submission Date

October 24, 2021

Acceptance Date

November 14, 2021

Published in Issue

Year 2021 Volume: 2 Number: 2

DOI

https://doi.org/10.53525/jster.1014186

IZ

https://izlik.org/JA69DL27RG

IEEE

[1]F. Bozkurt, “Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network”, Journal of Science, Technology and Engineering Research, vol. 2, no. 2, pp. 81–88, Dec. 2021, doi: 10.53525/jster.1014186.

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Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network

Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network

Abstract

Keywords

Dense Evrişimsel Ağ Kullanarak Kan Hücresi Görüntülerinden Kan Hücrelerinin Sınıflandırılması

Öz

Anahtar Kelimeler

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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