scholarly journals Classification of White Blood Cells Using Convolutional Neural Network

2018 ◽  
Vol 21 (1) ◽  
pp. 65-80
Author(s):  
Amin Edraki ◽  
AbolHassan Razminia ◽  
◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Blood Cells ◽  
White Blood Cells

scholarly journals Automatic Detection and Classification of White Blood Cells in Blood Smear Images Using Convolutional Neural Network

2019 ◽  
Vol 16 (Special Issue) ◽  
Author(s):  
Ramin Nateghi ◽  
Mansoor Fatehi ◽  
Ali Sadeghitabar ◽  
Romana Khosravi ◽  
Fattane Pourakpour
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Blood Cells ◽  
Blood Smear ◽  
White Blood Cells ◽  
Automatic Detection

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 IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Nighat Bibi ◽  
Misba Sikandar ◽  
Ikram Ud Din ◽  
Ahmad Almogren ◽  
Sikandar Ali
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Learning Algorithms ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Network Resources ◽  
And Performance

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients’ lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.

scholarly journals Detection of live breast cancer cells in brightfield microscopy images containing white blood cells by image analysis and deep learning

2021 ◽  
Author(s):  
Golnaz Moallem ◽  
Adity A. Pore ◽  
Anirudh Gangadhar ◽  
Hamed Sari-Sarraf ◽  
Siva A Vanapalli
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cancer Cells ◽  
Blood Cells ◽  
Breast Cancer Cells ◽  
White Blood Cells ◽  
Size Difference ◽  
Microscopy Images

Significance: Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug testing and molecular characterization. Aim: The goal is to develop and test deep learning (DL) approaches to detect unstained breast cancer cells in bright field microscopy images that contain white blood cells (WBCs). Approach: We tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate cancer cells from WBCs. The second approach is based on Faster Region-based Convolutional Neural Network (Faster R-CNN). Results: Both approaches detected cancer cells with high sensitivity and specificity with the Faster R-CNN being more efficient and suitable for deployment. The distinctive feature used by the CNN used to discriminate is cell size, however, in the absence of size difference, the CNN was found to be capable of learning other features. The Faster R-CNN was found to be robust with respect to intensity and contrast image transformations. Conclusions: CNN-based deep learning approaches could be potentially applied to detect patient-derived CTCs from images of blood samples.

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.

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