scholarly journals Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Ahmet Çınar ◽  
Seda Arslan Tuncer
Keyword(s):  
Blood Cells ◽  
Feature Vector ◽  
White Blood Cells ◽  
Structural Features ◽  
The Body ◽  
Diagnostic Process ◽  
Support Vector ◽  
Data Sets ◽  
Svm Model

AbstractWhite blood cells (WBC), which form the basis of the immune system, protect the body from foreign invaders and infectious diseases. While the number and structural features of WBCs can provide important information about the health of people, the ratio of the subtypes of these cells and observable deformations are a good indicator in the diagnostic process. The recognition of cells of the type of lymphocytes, neutrophils, eosinophils, basophils and monocytes is critical. In this article, Deep Learning based Hybrid CNN (Convololutional Neural Network) model is proposed for classification of eosinophils, lymphocytes, monocytes, and neutrophils WBCs. The model presented is based on pretrained Alexnet and Googlenet architectures. The feature vector in the last pooling layer of both CNN architectures has been merged, and the resulting feature vector is classified by the Support Vector Machine. To determine the superiority of the proposed method, the classification was also performed and compared using pretrained Alexnet and Googlenet. Hybrid Alexnet-Googlenet-SVM model provides higher accuracy than pretrained Alexnet and Googlenet. The proposed method has been tested with WBC images from Kaggle and LISC database. Accuracy and F1-score were 99.73%, 0.99 and 98.23%, 0.98 for both data sets, respectively.

scholarly journals WBC-CNN: Efficient CNN-Based Models to Classify White Blood Cells Subtypes

2021 ◽  
Vol 17 (13) ◽  
pp. 135-150
Author(s):  
Najla Alofi ◽  
Wafa Alonezi ◽  
Wedad Alawad
Keyword(s):  
Feature Extraction ◽  
Blood Cells ◽  
Classification Accuracy ◽  
White Blood Cells ◽  
Hybrid Models ◽  
Learning Technologies ◽  
Support Vector ◽  
Svm Model ◽  
Vector Machines

Blood is essential to life. The number of blood cells plays a significant role in observing an individual’s health status. Having a lower or higher number of blood cells than normal may be a sign of various diseases. Thus it is important to precisely classify blood cells and count them to diagnose different health conditions. In this paper, we focused on classifying white blood cells subtypes (WBC) which are the basic parts of the immune system. Classification of WBC subtypes is very useful for diagnosing diseases, infections, and disorders. Deep learning technologies have the potential to enhance the process and results of WBC classification. This study presented two fine-tuned CNN models and four hybrid CNN-based models to classify WBC. The VGG-16 and MobileNet are the CNN architectures used for both feature extraction and classification in fine-tuned models. The same CNN architectures are used for feature extraction in hybrid models; however, the Support Vector Machines (SVM) and the Quadratic Discriminant Analysis (QDA) are the classifiers used for classification. Among all models, the fine-tuned VGG-16 performs best, its classification accuracy is 99.81%. Our hybrid models are efficient in detecting WBC as well. 98.44% is the classification accuracy of the VGG-16+SVM model, and 98.19% is the accuracy of the MobileNet+SVM.

scholarly journals New Segmentation and Feature Extraction Algorithm for the Classification of White Blood Cells in Peripheral Smear Images

2021 ◽  
Author(s):  
Eslam Tavakoli ◽  
Ali Ghaffari ◽  
Seyedeh-Zahra Mousavi Kouzehkanan ◽  
Reshad Hosseini
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Dice Similarity Coefficient ◽  
Second Phase ◽  
Peripheral Smear ◽  
Color Features ◽  
Svm Model ◽  
Extraction Algorithm ◽  
Processing Techniques

This article addresses a new method for classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it which is located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shape and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.47 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate.

scholarly journals Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

2019 ◽  
Vol 9 (2) ◽  
pp. 173
Author(s):  
Apri Nur Liyantoko ◽  
Ika Candradewi ◽  
Agus Harjoko
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Back Propagation ◽  
Cell Types ◽  
Diagnostic Process ◽  
The Subject ◽  
Processing Techniques

 Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and  multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

scholarly journals New Segmentation and Feature Extraction Algorithm for Classification of White Blood Cells in Peripheral Smear Images

2021 ◽  
Author(s):  
Eslam Tavakoli ◽  
Ali Ghaffari ◽  
Zahra Mousavi Kouzehkanan ◽  
Reshad Hosseini
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Dice Similarity Coefficient ◽  
Second Phase ◽  
Peripheral Smear ◽  
Color Features ◽  
Svm Model ◽  
Extraction Algorithm ◽  
Processing Techniques

Abstract This article addresses a new method for the classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it which is located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shapes and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.65 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with the Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate.

scholarly journals New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sajad Tavakoli ◽  
Ali Ghaffari ◽  
Zahra Mousavi Kouzehkanan ◽  
Reshad Hosseini
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Dice Similarity Coefficient ◽  
Second Phase ◽  
Peripheral Smear ◽  
Color Features ◽  
Svm Model ◽  
Extraction Algorithm ◽  
Processing Techniques

AbstractThis article addresses a new method for the classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shapes and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.65%, 92.21%, and 94.20%, respectively. Besides, we show that the proposed method possesses more generalization power than pre-trained CNN models. It is worth mentioning that the hyperparameters of the classifier are fixed only with the Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets.

scholarly journals An Assessment of Gpu and Cpu based Convolutional Neural Network for Classification of White Blood Cells

2020 ◽  
Vol 9 (7) ◽  
pp. 554-559
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Image Data ◽  
The Body ◽  
Machine Learning Techniques ◽  
Vast Number

White blood cell (Leukocytes) is made up of bone marrow located in the blood and lymph tissue. They are portion of the human body’s immune system, thereby helping the body system to fight against infection and other related diseases. The number of leukocytes in the blood is usually part of a complete blood cell (CBC) test, which may be used to check for conditions such as infection, inflammation, allergies, and leukemia. Automation of variance count of leukocytes offers valuable information to medical pathologist to diagnose and treat of many blood based diseases. Early characterization and classification of blood sample is a major lacuna in the medical field, giving rise to lots of challenges for pathologist to adequately predict blood based disease. Several successful efforts have been made to address the aforementioned challenges with the use of machine learning generally and Convolution Neural Network in particular. However the processor configuration which can result in real time, and accurate classification of the high dimensional pattern is imminent, and a vast number of researchers are not explicit on the system configuration used to obtain the result in their report, which is the crux of this research. In this research,12,500 augment images of blood cells was obtained from the Kaggle Repository online. The leukocytes are contained in the blood smear image and categorized into five major types of their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte. The color, geometric and texture features are used by the pathologists to differentiate the leukocytes. The Simulation was done using python programing language and python libraries including Keras, pandas, sklearn, numpy, scipy and matplot for potting of graphs of results. The simulation was done on both CPU and GPU processor to compare the performance of the processors on CNNs based classification of the data. While CPU has faster clock speed GPU has more cores. Hence the evaluation metrics used which are precision, specificity, sensitivity, training accuracy and validation accuracy revealed that GPU processor outperforms CPU in terms of the stated metrics of comparison. Therefore a high configuration processor (GPU), which handles graphics better is recommended for processing image data that involves the use of machine learning techniques

scholarly journals Detection of Blood Cancer Cells Using Microscopic Images

2021 ◽  
Vol 9 (8) ◽  
pp. 995-1003
Author(s):  
Vidyashree M S
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cancer Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Blood Cancer ◽  
Acute Myeloid

Abstract: Blood Cancer cells forming a tissue is called lymphoma. Thus, disease decreases the cells to fight against the infection or cancer blood cells. Blood cancer is also categorized in too many types. The two main categories of blood cancer are Acute Lymphocytic Lymphoma and Acute Myeloid Lymphoma. In this project proposes a approach that robotic detects and segments the nucleolus from white blood cells in the microscopic Blood images. Here in this project, we have used the two Machine learning algorithms that are k-means algorithm, Support vector machine algorithm. K-mean algorithm is use for segmentation and clustering. Support vector machine algorithm is used for classification. Keywords: k-means, Support vector machine, Lymphoma, Acute Lymphocytic Lymphoma, Machine Learning

scholarly journals EFFECT OF ZINC ELEMENT AND DEXAMETHASONE ON SOME HEMATOLOGICAL BIOCHEMICAL TESTS IN RABBITS MALE

2016 ◽  
Vol 47 (6) ◽  
Author(s):  
D. S. Dheyab
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
The Body ◽  
Control Group ◽  
Blood Collection ◽  
Biochemical Tests ◽  
Histological Changes ◽  
Spleen Tissue ◽  
Cholesterol Levels ◽  
Short Time

This study was conducted to investigate the effect of zinc in dose 15mg/kg.bw daily  taken by the mouth and dexamethasone 4mgIkg.Bw by injection for 30days on some hematological biochemical tests and some histological changes of liver spleen in male rabbits. Thirty rabbits were used that divided into 3 randomized groups (each group contain 10 male rabbits ). Control group was taken normal food and water, Zinc group that gave zinc at dose of 15mg/kg.BW/daily/oral on 1, 2, 3, 4 weeks. Dexamethasone with zinc group : Employ dexamethasone 4mg/Kg.Bw . I.M dialy for 1 and 2 weeks for experiment and at  3, 4th weeks they gave zn 15mg/lKg.Bw day/orally. Blood samples were taken from the heart directly in 2 and 4weeks to examine packed cell volume (pcv), white blood cells (WBCs), Red blood cells (RBCs) with differential Leuckcyte count.separation blood collection to plasma and examine glucose mg/dl , cholesterol mg/dl. In histological tests, rabbits were killed and separate their organs tissue from the body to examine liver and spleen. The results revealed  a decrease in level of RBCs, pcv after treatment with zinc 15, mg/Kg.Bw orally (zinc group) and increase in WBCs with differential leuckocyte count specially neutrophil cell, while biochemical tests show increase in glucose and cholesterol levels after treatment with dexamethasone 4mglkgBw. I/M seen increase in counts of RBCs , PCV, WBCs and differential lenkocyte count and decrease in glucose with cholesterol parameters, histological changes show change in liver after treatment by dexamethasone 4mglKg.Bw ,spleen tissue seen necrosis and pigmentation with hemorrhage after take dexamethasone 4mglkg in (dexamethasone + zinc group). Results also showed that zinc enhanced the immune system in at normal dose for limited time  because of its effect on other mineral such as copper and causes anemia , while the dexamethasone is a drug used for antianflammatory but for a short time.                                                                                                                           

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