Title: Vision-based malaria parasite image analysis: a systematic review

Authors: Priyadarshini Adyasha Pattanaik; Tripti Swarnkar

Addresses: Department of Computer Science & Engineering, SOA University, Bhubaneswar – 751030, India ' Department of Computer Science & Engineering, SOA University, Bhubaneswar – 751030, India

Abstract: Background: Malaria is one of the classic neglected serious diseases in many developing countries. The early stage of disease detection, accurate parasite count, detection of the aggressiveness of the disease, technical limitations, lack of expertise in malaria diagnosis and smart tools, lack of good quality healthcare services, funds so on are the challenges found during malaria diagnosis that requires a deeper analysis. Objectives: This paper aims to give a review of the automated diagnosis or visual inspection of malaria parasites using histology images of thin or thick blood film smears. Methods and Results: Various computer -aided diagnosis techniques are in use to solve tasks meticulously in a stratified description paradigm using non-linear transformation architectures. Conclusion: This work elaborates a comprehensive study of various computer vision diagnostic approaches already proposed in this field with a future direction for better quicker malaria identification.

Keywords: malaria parasites; microscopy analysis; computer vision diagnosis; deep learning.

DOI: 10.1504/IJBRA.2019.097987

International Journal of Bioinformatics Research and Applications, 2019 Vol.15 No.1, pp.1 - 32

Received: 02 Nov 2016
Accepted: 27 Nov 2017

Published online: 20 Feb 2019 *

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