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

Year 2021, , 81 - 88, 21.12.2021

Ferhat Bozkurt

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

Cited By: 4

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

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

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