The week's pick
Reseach Article
Analysis of Accuracy of Differential Count of White Blood Cells using Support Vector Machine
| Emerging Trends in Computing |
| Foundation of Computer Science USA |
| ETC2016 - Number 1 |
| March 2017 |
| Authors: Kiran J. Waghmare, Gautam Gawas, Aishwarya Rana, Dilraj Velip, Tulsi Acharya, Vivek Kumar Jaiswal |
| df538328-d332-4d36-ade3-30c01f1bbe5c |
Kiran J. Waghmare, Gautam Gawas, Aishwarya Rana, Dilraj Velip, Tulsi Acharya, Vivek Kumar Jaiswal . Analysis of Accuracy of Differential Count of White Blood Cells using Support Vector Machine. Emerging Trends in Computing. ETC2016, 1 (March 2017), 29-33.
Abstract
Blood smear is a clinical test performed on microscopic digital images routinely investigated by hematologists to diagnose most blood diseases. Blood smear generally composed of Red blood cells (RBC), White blood cells (WBC) and Platelets. The quantities of WBC cell are counted in a sample blood smear and necessary information calculated manually by the doctor for diagnosis for various diseases. So, this differential counting of WBC cells plays very vital role to get high precision results. The main objective of this paper is to construct the computerized automated software to evaluate and classify a blood smear for differential counting of WBC with the help of Digital Image Processing. We also focus on Image segmentation and Feature extraction to classify the different types of WBC at its accuracy. To check the efficiency and robustness of automated system, the comparison between manual and automated counting is done, which gives the 80% accuracy for automated system
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