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

Ferhat BOZKURT

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

Makale detayları

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

Yazarlar:

Ferhat BOZKURT

Yayıncı:

Mehmet BULUT

Ana Başlıklar:

Özel bilgisayar yöntemleri

Dergi:

Journal of Science, Technology and Engineering Research 2021 Vol.2 Issue 2, pp.81-88

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000495126

Sıklık:

İki Yıllık

eISSN:

2717-8404

DOI:

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

Özet:

 

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.

 

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.

Makale detayları

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

Yazarlar: Ferhat BOZKURT

Yayıncı: Mehmet BULUT

Ana Başlıklar: Özel bilgisayar yöntemleri

Dergi: Journal of Science, Technology and Engineering Research 2021 Vol.2 Issue 2, pp.81-88

Belge Tipi: Makale

Yayın Tarihi: 12/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000495126

Sıklık: İki Yıllık

eISSN: 2717-8404

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

Sayı

Yazarlar:

Ferhat BOZKURT

Yayıncı:

Mehmet BULUT

Ana Başlıklar:

Özel bilgisayar yöntemleri

Dergi:

Journal of Science, Technology and Engineering Research 2021 Vol.2 Issue 2, pp.81-88

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000495126

Sıklık:

İki Yıllık

eISSN:

2717-8404

DOI:

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