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Automated Malaria Cell Image Classification Using Convolutional Neural Networks
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  • V M Sibinraj,
  • Fiza Gafoor,
  • P K Anandhu,
  • Dr. Anoop V. S.
V M Sibinraj
NLP for Social Good Lab School of Digital Sciences, Kerala University of Digital Sciences-Innovation and Technology
Fiza Gafoor
NLP for Social Good Lab School of Digital Sciences Kerala University of Digital Sciences-Innovation and Technology Thiruvananthapuram
P K Anandhu
NLP for Social Good Lab School of Digital Sciences Kerala University of Digital Sciences-Innovation and Technology Thiruvananthapuram
Dr. Anoop V. S.
NLP for Social Good Lab School of Digital Sciences Kerala University of Digital Sciences-Innovation and Technology Thiruvananthapuram

Corresponding Author:[email protected]

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Abstract

Malaria is a serious and potentially deadly vector-borne disease that affects millions of people around the world. According to the latest report by the World Health Organization, there are an estimated 247 million cases of malaria worldwide. Malaria is caused by Plasmodium parasites and is transmitted by the bite of Anopheles mosquitoes. Early detection of Malaria is crucial to detect to treat the same early and prevent any potential spread that may cause serious fatalities. In the past, there are many approaches reported that uses machine learning approaches for early detection of Malaria from images. But still there are avenues where the detection accuracies are less and may need more advanced models for easy and accurate detection. This work proposes an approach for automated classification of Malaria from cell images using Convolutional Neural Networks (CNN). When compared with some state-of-the-art approaches, our proposed approach shows better classification accuracy and outperformed them.
28 May 2024Submitted to TechRxiv
03 Jun 2024Published in TechRxiv