International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 184 - Issue 13 |
Published: May 2022 |
Authors: Praburam K. Varadharajan, K. Harini |
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Praburam K. Varadharajan, K. Harini . Machine Learning Approach for Classification and Identification of Blood Cells. International Journal of Computer Applications. 184, 13 (May 2022), 34-37. DOI=10.5120/ijca2022922117
@article{ 10.5120/ijca2022922117, author = { Praburam K. Varadharajan,K. Harini }, title = { Machine Learning Approach for Classification and Identification of Blood Cells }, journal = { International Journal of Computer Applications }, year = { 2022 }, volume = { 184 }, number = { 13 }, pages = { 34-37 }, doi = { 10.5120/ijca2022922117 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2022 %A Praburam K. Varadharajan %A K. Harini %T Machine Learning Approach for Classification and Identification of Blood Cells%T %J International Journal of Computer Applications %V 184 %N 13 %P 34-37 %R 10.5120/ijca2022922117 %I Foundation of Computer Science (FCS), NY, USA
In the medical field, blood testing is considered one of the most important clinical examinations. A complete blood cell count is important for any medical diagnosis. Traditionally manual equipment is used to do this task which is time-consuming. Therefore, there is a need to research for an automated blood cell detection system that will help physicians to solve the problem efficiently. This paper presents a machine learning approach for the automatic identification and classification of three types of blood cells using a Single-shot Multi-Box detector (SSD) network. This framework has been trained on the BCCD Dataset of blood smear images to automatically identify red blood cells, White blood cells, and platelets.