scholarly journals Real‐Time Stain‐Free Classification of Cancer Cells and Blood Cells Using Interferometric Phase Microscopy and Machine Learning

2020 ◽  
Author(s):  
Noga Nissim ◽  
Matan Dudaie ◽  
Itay Barnea ◽  
Natan T. Shaked
Keyword(s):  
Machine Learning ◽  
Cancer Cells ◽  
Real Time ◽  
Blood Cells ◽  
Phase Microscopy ◽  
Interferometric Phase ◽  
Free Classification

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

Segmentation of Leukemia Cells Using Clustering

2019 ◽  
Vol 10 (2) ◽  
pp. 39-48
Author(s):  
Eman Mostafa ◽  
Heba A. Tag El-Dien
Keyword(s):  
Bone Marrow ◽  
Cancer Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Leukemia Cells ◽  
Microscopic Image ◽  
Processing Stage ◽  
Clustering Techniques ◽  
Fuzzy C Means

Leukemia is a blood cancer which is defined as an irregular augment of undeveloped white blood cells called “blasts.” It develops in the bone marrow, which is responsible for blood cell generation including leukocytes and white blood cells. The early diagnosis of leukemia greatly helps in the treatment. Accordingly, researchers are interested in developing advanced and accurate automated techniques for localizing such abnormal blood cells. Subsequently, image segmentation becomes an important image processing stage for successful feature extraction and classification of leukemia in further stages. It aims to separate cancer cells by segmenting the microscopic image into background and cancer cells that are known as the region of interested (ROI). In this article, the cancer blood cells were segmented using two separated clustering techniques, namely the K-means and Fuzzy-c-means techniques. Then, the results of these techniques were compared to in terms of different segmentation metrics, such as the Dice, Jac, specificity, sensitivity, and accuracy. The results proved that the k-means provided better performance in leukemia blood cells segmentation as it achieved an accuracy of 99.8% compared to 99.6% with the fuzzy c-means.

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