Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images.

Thaqifah Ahmad Aris, Aimi Salihah Abdul Nasir, Wan Azani Mustafa, Mohd Yusoff Mashor, Edy Victor Haryanto, Zeehaida Mohamed

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Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Thaqifah Ahmad ArisFaculty of Electrical Engineering and Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar 02100, Malaysia.

Aimi Salihah Abdul NasirFaculty of Electrical Engineering and Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar 02100, Malaysia.ORCID 0000-0001-9291-0228

Wan Azani MustafaFaculty of Electrical Engineering and Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar 02100, Malaysia.ORCID 0000-0001-7260-6085

Mohd Yusoff MashorFaculty of Electronic Engineering and Technology, Universiti Malaysia Perlis, Pauh Putra Campus, Arau 02600, Malaysia.

Edy Victor HaryantoFaculty of Engineering and Computer Science, Universitas Potensi Utama, Medan 20241, Indonesia.ORCID 0000-0001-8317-3545

Zeehaida MohamedDepartment of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Malaria is a pressing medical issue in tropical and subtropical regions. Currently, the manual microscopic examination remains the gold standard malaria diagnosis method. Nevertheless, this procedure required highly skilled lab technicians to prepare and examine the slides. Therefore, a framework encompassing image processing and machine learning is proposed due to inconsistencies in manual inspection, counting, and staging. Here, a standardized segmentation framework utilizing thresholding and clustering is developed to segment parasites' stages of

Indexed as

clusteringimage segmentationmachine learningplasmodium parasitespecies and stagingthresholding

Identifiers

PMID36766620

DOI10.3390/diagnostics13030511

PMCPMC9913904

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