UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 9 Issue 4
April-2022
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2204685


Registration ID:
401249

Page Number

g588-g594

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Title

Malaria Parasite Detection using Deep Learning

Abstract

Abstract : Malaria is a deadly disease that affects hundreds of millions of people each year all around the world. It can be lethal if not treated promptly. Despite recent advances in malaria diagnostics, the most prevalent method for detecting malaria is microscopy. Unfortunately, the accuracy of microscopic diagnostics is dependent on the microscopist's skill, which limits the speed with which malaria can be diagnosed. With the advancement of Artificial Intelligence tools, particularly Deep Learning approaches, it is now possible to reduce costs while increasing overall accuracy. By adding deep neural networks, we may improve malaria diagnosis from patches segmented from digital microscopic images of red blood cells. In relation to clinical procedures that necessitate the time-consuming manual extraction of features, the suggested technique use a deep learning technique that extracts features from pixels and classifies the red blood cell directly from segmented patches. This study's dataset was obtained from the Kaggle online database.

Key Words

Deep learning, Malaria classification, Convolutional neural network , Red blood cells, Thin blood smears.

Cite This Article

"Malaria Parasite Detection using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.g588-g594, April-2022, Available :http://www.jetir.org/papers/JETIR2204685.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Malaria Parasite Detection using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. ppg588-g594, April-2022, Available at : http://www.jetir.org/papers/JETIR2204685.pdf

Publication Details

Published Paper ID: JETIR2204685
Registration ID: 401249
Published In: Volume 9 | Issue 4 | Year April-2022
DOI (Digital Object Identifier):
Page No: g588-g594
Country: Mumbai, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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