Development of intelligent suite for malaria pathogen detection in microscopy images.
Automation
Medical
Software
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
11 Oct 2024
11 Oct 2024
Historique:
received:
27
09
2023
accepted:
09
10
2024
medline:
12
10
2024
pubmed:
12
10
2024
entrez:
11
10
2024
Statut:
epublish
Résumé
The identification of malaria infection using microscope images of blood smears is considered as a 'gold standard'. The diagnosis of malaria needs expert microscopists which are scarce in remote areas where malaria is endemic. Therefore, it is desirable to automate the repetitive task of pathogen detection in the blood samples received as microscope images. This study provides an easy to use and deploy method for implementing a malaria pathogen detection software- the Intelligent Suite. The Intelligent Suite features a graphical user interface (GUI) implemented using 'cvui' library to interact with the OpenVINO's inference engine for model optimisation and deployment across several inference devices. The intelligent Suite uses a custom YOLO-mp-3l model trained on Darknet framework for detection of malaria pathogen in thick smear microscope images. Moreover, the Intelligent Suite provides user interface for inference device/mode selection, alter model parameters, and generate detection reports along with the model performance metrics. The Intelligent Suite was executed on a CPU computer with model inference running on a plug-and-play Neural Compute Stick (NCS2) and performance reported.
Identifiants
pubmed: 39394413
doi: 10.1038/s41598-024-75933-w
pii: 10.1038/s41598-024-75933-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
23821Informations de copyright
© 2024. The Author(s).
Références
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