INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Machine learning has shown enormous development in recent years. Using the concepts of machine learning, one can find solutions to the use of year-old hardcoded algorithms and manual analysis that have a chance of developing false positives or vice-versa. With the advancements in medical imaging and computer technology, medical image processing has become increasingly important in diagnosis. X-ray radiography, CT, and MRI produce massive volumes of medical images. Studying the detection of red blood cells through medical imaging has taken us to explore deep neural networks in combination with the concepts of machine learning. The concept of the Efficient-Det-D0 Neural Network has been thoroughly revised in the paper to detect red blood cells. Microscopic imaging of the blood cells has resulted in a faster analysis of the cells than the conventional methods. This technique has proven to be a boon for microfluidic point-of-care medical devices.
Keywords:
Deep Learning, Blood Cell, Object Detection, Efficient-Det, Medical Imaging, Computer Vision, OpenCV, Python
Cite Article:
"Blood Cell Detection in Microscopic Images", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 7, page no.476 - 478, July-2022, Available :http://www.ijsdr.org/papers/IJSDR2207065.pdf
Downloads:
000336256
Publication Details:
Published Paper ID: IJSDR2207065
Registration ID:201002
Published In: Volume 7 Issue 7, July-2022
DOI (Digital Object Identifier):
Page No: 476 - 478
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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