Malaria Parasite Detection in Microscopic Blood Smear Images using Deep Learning Approach

Dr. M. Praneesh; Sai Krishna P K; Febina. N; Ashwanth.V

doi:10.32628/CSEIT2410286

Authors

Dr. M. Praneesh Assistant Professor, PG & Research Dept of Computer Science, Sri Ramakrishna College of Arts & Science, Coimbatore, Tamil Nadu, India Author
Sai Krishna P K PG Scholar, PG & Research Dept of Computer Science, Sri Ramakrishna College of Arts & Science, Coimbatore, Tamil Nadu, India Author
Febina. N PG Scholar, PG & Research Dept of Computer Science, Sri Ramakrishna College of Arts & Science, Coimbatore, Tamil Nadu, India Author
Ashwanth.V PG Scholar, PG & Research Dept of Computer Science, Sri Ramakrishna College of Arts & Science, Coimbatore, Tamil Nadu, India Author

DOI:

https://doi.org/10.32628/CSEIT2410286

Keywords:

Malaria, Automated diagnosis, Blood smears, Deep Learning

Abstract

Malaria remains a significant global health concern, posing formidable challenges to healthcare systems. Conventional diagnostic methods rely on manual examination of blood smears under a microscope, a process prone to inefficiencies and subjectivity. Despite prior attempts to leverage Deep Learning algorithms for malaria diagnosis, practical performance has often fallen short. This paper presents a novel machine learning model centred on Convolutional Neural Networks (CNNs) designed to automate the classification and prediction of infected cells in thin blood smears on standard microscope slides. Through rigorous ten-fold cross-validation with 27,558 single-cell images. This paper reviews various image processing techniques employed for the detection of malaria infection in humans, presenting a comparative analysis of these methods

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