Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images.

ABSTRACT: Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer-aided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI). In contrast, Convolutional Neural Networks (CNN), a c

SUBMITTER: Rajaraman S 

PROVIDER: S-EPMC5907772 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature