scholarly journals Improved White Blood Cells Classification based on Pre-trained Deep Learning Models

2020 ◽  
Vol 16 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Ensaf H. Mohamed ◽  
Wessam H. El-Behaidy ◽  
Ghada Khoriba ◽  
Jie Li
Keyword(s):  
Machine Learning ◽  
Blood Cells ◽  
White Blood Cells ◽  
Evaluation Process ◽  
Cell Detection ◽  
Learning Classifier ◽  
Proposed Model ◽  
Higher Rank ◽  
Fully Connected ◽  
Automated Methods

Leukocytes, or white blood cells (WBCs), are microscopic organisms that fight against infectious disease, bacteria, viruses, and others. The manual method to classify and count WBCs is tedious, time-consuming and may has inaccurate results, whereas the automated methods are costly. The objective of this work is to automatically identify and classify WBCs in a microscopic image into four types with higher accuracy. BCCD is the used dataset in this study, which is a scaled down blood cell detection dataset. BCCD is firstly pre-processed by passing through several processes such as segmentation and augmentation,then it is passed to the proposed model. Our model combines the privilege of deep models in automatically extracting features with the higher classification accuracy of traditional machine learning classifiers.The proposed model consists of two main layers; a shallow tuning pre-trained model and a traditional machine learning classifier on top of it. Here, ten different pretrained models with six different machine learning are used in this study. Moreover, the fully connected network (FCN) of pretrained models is used as a baseline classifier for comparison. The evaluation process shows that the hybrid between MobileNet-224 as feature extractor with logistic regression as classifier has a higher rank-1 accuracy with 97.03%. Besides, the proposed hybrid model outperformed the baseline FCN with 25.78% on average.

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

Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms

The Analyst ◽  
2020 ◽  
Vol 145 (21) ◽  
pp. 6955-6967
Author(s):  
Adam H. Agbaria ◽  
Guy Beck ◽  
Itshak Lapidot ◽  
Daniel H. Rich ◽  
Joseph Kapelushnik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ftir Spectroscopy ◽  
Blood Cells ◽  
Learning Algorithms ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Infrared Microscopy

Rapid and objective diagnosis of the etiology of inaccessible infections by analyzing WBCs spectra, measured by FTIR spectroscopy, using machine-learning.

scholarly journals Raabin-WBC: a large free access dataset of white blood cells from normal peripheral blood

2021 ◽  
Author(s):  
Seyedeh-Zahra Mousavi Kouzehkanan ◽  
Sepehr Saghari ◽  
Eslam Tavakoli ◽  
Peyman Rostami ◽  
Mohammadjavad AbbasZadeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Peripheral Blood ◽  
Blood Cells ◽  
Deep Neural Networks ◽  
New Technologies ◽  
White Blood Cells ◽  
Well Being ◽  
Free Access ◽  
Normal Peripheral Blood

Accurate and early detection of peripheral white blood cell anomalies plays a crucial role in the evaluation of an individual's well-being. The emergence of new technologies such as artificial intelligence can be very effective in achieving this. In this regard, most of the state-of-the-art methods use deep neural networks. Data can significantly influence the performance and generalization power of machine learning approaches, especially deep neural networks. To that end, we collected a large free available dataset of white blood cells from normal peripheral blood samples called Raabin-WBC. Our dataset contains about 40000 white blood cells and artifacts (color spots). To reassure correct data, a significant number of cells were labeled by two experts, and the ground truth of nucleus and cytoplasm were extracted by experts for some cells (about 1145), as well. To provide the necessary diversity, various smears have been imaged. Hence, two different cameras and two different microscopes were used. The Raabin-WBC dataset can be used for different machine learning tasks such as classification, detection, segmentation, and localization. We also did some primary deep learning experiments on Raabin-WBC, and we showed how the generalization power of machine learning methods, especially deep neural networks, was affected by the mentioned diversity.

scholarly journals Raabin-WBC: a large free access dataset of white blood cells from normal peripheral blood

2021 ◽  
Author(s):  
Zahra Mousavi Kouzehkanan ◽  
Sepehr Saghari ◽  
Eslam Tavakoli ◽  
Peyman Rostami ◽  
Mohammadjavad Abaszadeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Peripheral Blood ◽  
Blood Cells ◽  
Deep Neural Networks ◽  
New Technologies ◽  
White Blood Cells ◽  
Well Being ◽  
Free Access ◽  
Normal Peripheral Blood

Abstract Accurate and early detection of peripheral white blood cell anomalies plays a crucial role in the evaluation of an individual's well-being. The emergence of new technologies such as artificial intelligence can be very effective in achieving this. In this regard, most of the state-of-the-art methods use deep neural networks. Data can significantly influence the performance and generalization power of machine learning approaches, especially deep neural networks. To that end, we collected a large free available dataset of white blood cells from normal peripheral blood samples called Raabin-WBC. Our dataset contains about 40000 white blood cells and artifacts (color spots). To reassure correct data, a significant number of cells were labeled by two experts, and the ground truth of nucleus and cytoplasm were extracted by experts for some cells (about 1145), as well. To provide the necessary diversity, various smears have been imaged. Hence, two different cameras and two different microscopes were used. The Raabin-WBC dataset can be used for different machine learning tasks such as classification, detection, segmentation, and localization. We also did some primary deep learning experiments on Raabin-WBC, and we showed how the generalization power of machine learning methods, especially deep neural networks, was affected by the mentioned diversity.

scholarly journals Label‐Free Identification of White Blood Cells Using Machine Learning

2019 ◽  
Author(s):  
Mariam Nassar ◽  
Minh Doan ◽  
Andrew Filby ◽  
Olaf Wolkenhauer ◽  
Darin K. Fogg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Cells ◽  
White Blood Cells ◽  
Label Free

scholarly journals Classification of Human White Blood Cells Using Machine Learning for Stain‐Free Imaging Flow Cytometry

2019 ◽  
Vol 97 (3) ◽  
pp. 308-319 ◽  
Author(s):  
Maxim Lippeveld ◽  
Carly Knill ◽  
Emma Ladlow ◽  
Andrew Fuller ◽  
Louise J Michaelis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Blood Cells ◽  
White Blood Cells ◽  
Imaging Flow Cytometry ◽  
Human White Blood Cells

scholarly journals Leukemia Diagnosis using Machine Learning Classifiers Based on Correlation Attribute Eval Feature Selection

2021 ◽  
pp. 52-65
Author(s):  
Revella E. A. Armya ◽  
Adnan Mohsin Abdulazeez ◽  
Amira Bibo Sallow ◽  
Diyar Qader Zeebaree
Keyword(s):  
Machine Learning ◽  
Blood Cells ◽  
Naive Bayes ◽  
White Blood Cells ◽  
Naïve Bayes ◽  
Primary Objective ◽  
Bayes Classifier ◽  
Machine Learning Classifiers ◽  
Leukemia Diagnosis ◽  
Advanced Stages

Leukemia refers to a disease that affects the white blood cells (WBC) in the bone marrow and/or blood. Blood cell disorders are often detected in advanced stages as the number of cancer cells is much higher than the number of normal blood cells. Identifying malignant cells is critical for diagnosing leukemia and determining its progression. This paper used machine learning with classifiers to detect leukemia types as a result, it can save both patients and physicians time and money. The primary objective of this paper is to determine the most effective methods for leukemia detection. The WEKA application was used to evaluate and analyze five classifiers (J48, KNN, SVM, Random Forest, and Naïve Bayes classifiers). The results were respectively as follows: 83.33%, 87.5%, 95.83%, 88.88%, and 98.61%, with the Naïve Bayes classifier achieving the highest accuracy; however, accuracy varies according to the shape and size of the sample and the algorithm used to classify the leukemia types.

scholarly journals Correction: Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms

The Analyst ◽  
2020 ◽  
Vol 145 (22) ◽  
pp. 7447-7447
Author(s):  
Adam H. Agbaria ◽  
Guy Beck ◽  
Itshak Lapidot ◽  
Daniel H. Rich ◽  
Joseph Kapelushnik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Cells ◽  
Learning Algorithms ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Infrared Microscopy

Correction for ‘Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms’ by Adam H. Agbaria et al., Analyst, 2020, DOI: 10.1039/D0AN00752H.

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