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Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks
2021
Biological Imaging
Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC)
doi:10.1017/s2633903x21000015
pmid:35036920
pmcid:PMC8724263
fatcat:3zqw2w2uxva2xo4yr5k6fidovi