Automatic White Blood Cell Detection and Identification Using Convolutional Neural Network

2018 ◽  
Author(s):  
Filip Novoselnik ◽  
Ratko Grbic ◽  
Irena Galic ◽  
Filip Doric
Keyword(s):  
Neural Network ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
White Blood Cell ◽  
Cell Detection ◽  
Detection And Identification

scholarly journals Detection and Classification of White Blood Cells through Deep Learning Techniques

2020 ◽  
Vol 16 (15) ◽  
pp. 94
Author(s):  
Samir Abou El-Seoud ◽  
Muaad Hammuda Siala ◽  
Gerard McKee
Keyword(s):  
Neural Network ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
White Blood Cell ◽  
Blood Cells ◽  
Human Life ◽  
White Blood Cells ◽  
Learning Techniques ◽  
Normal White Blood Cell ◽  
High Level

Leukemia is one of the deadliest diseases in human life, it is a type of cancer that hits blood cells. The task of diagnosing Leukemia is time consuming and tedious for doctors; it is also challenging to determine the level and type of Leukemia. The diagnoses of Leukemia are achieved through identifying the changes on the White blood Cells (WBC). WBCs are divided into five types: Neutrophils, Eosinophils, Basophils, Monocytes, and Lymphocytes. In this paper, the authors propose a Convolutional Neural Network to detect and classify normal white blood cells. The program will learn about the shape and type of normal WBC by performing the following two tasks. The first task is identifying high level features of a normal white blood cell. The second task is classifying the normal white blood cell according to its type. Using a Convolutional Neural Network CNN, the system will be able to detect normal WBCs by comparing them with the high-level features of normal WBC. This process of identifying and classifying WBC can be vital for doctors and medical staff to make a decision. The proposed network achieves an accuracy up to 96.78% with a dataset including 10,000 blood cell images.

scholarly journals Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

2019 ◽  
Vol 25 (5) ◽  
pp. 63-68 ◽  
Author(s):  
Mesut Togacar ◽  
Burhan Ergen ◽  
Mehmet Emre Sertkaya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Immune System ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
White Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Network Models ◽  
Neural Network Models

The white blood cells produced in the bone marrow and lymphoid tissue known as leucocytes are an important part of the immune system to protect the body against foreign invaders and infectious disease. These cells, which do not have color, have a few days or several weeks of life. A lot of clinic experience is required for a doctor to detect the amount of white blood cells in human blood and classify it. Thus, early and accurate diagnosis can be made in the formation of various disease types, including infection on the immune system, such as anemia and leukemia, while evaluating and determining the disease of a patient. The white blood cells can be separated into four subclasses, such as Eosinophil, Lymphocyte, Monocyte, and Neutrophil. This study focuses on the separation of the white blood cell images by the classification process using convolutional neural network models, which is a deep learning model. A deep learning network, which is slow in the training step due to the complex architecture, but fast in the test step, is used for the feature extraction instead of intricate methods. For the subclass separation of white blood cells, the experimental results show that the AlexNet architecture gives the correct recognition rate among the convolutional neural network architectures tested in the study. Various classifiers are performed on the features derived from the AlexNet architecture to evaluate the classification performance. The best performance in the classification of white blood cells is given by the quadratic discriminant analysis classifier with the accuracy of 97.78 %.

scholarly journals Classifying White Blood Cells in Blood Smear Images using a Convolutional Neural Network

2019 ◽  
Vol 8 (9S) ◽  
pp. 825-828
Keyword(s):  
Neural Network ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
White Blood Cell ◽  
Blood Cells ◽  
Blood Smear ◽  
Binary Classification ◽  
White Blood Cells ◽  
Classification Problem ◽  
Classification Task

We have tried to automate the classification task of white blood cells by using a Convolutional Neural Network. We have divided white blood cell classification in two types of problems, a binary class problem and a 4-classification problem. In binary class problem we classify white blood cell as either mononuclear or Grenrecules. In 4-classification problem where cells are classified into their subtypes (monocytes, lymphocytes, neutrophils, basophils and eosinophils). In our experiment we were able to achieve validation accuracy of 100% in binary classification and 98.40 in multiple classifications.

Sign in / Sign up

Export Citation Format

Share Document