ArticleScientific reports2024
Development of intelligent suite for malaria pathogen detection in microscopy images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- A Systematic Literature Review of You Only Look Once Architectures (v1-v12) in Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Review
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10 authors.
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Abstract
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.
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