Objectification of evaluation criteria in microscopic agglutination test using deep learning
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Risa Nakano1, Yuji Oyamada1, Ryo Ozuru2
1Graduate School of Engineering, Tottori University, Japan.
Abstract:
We aim to objectify the evaluation criteria of agglutination rate estimation in the Microscopic Agglutination Test (MAT). This study proposes a deep learning method that extracts free leptospires from dark-field microscopic images and calculates the agglutination rate. The experiments show the effect of objectification with real pictures.
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