CPC G16H 10/40 (2018.01) [G06F 16/51 (2019.01); G06F 18/2148 (2023.01); G06F 18/40 (2023.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06T 7/73 (2017.01); G06V 10/945 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G16H 30/40 (2018.01); G06N 20/00 (2019.01); G06T 2207/10056 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20092 (2013.01); G06T 2207/20132 (2013.01); G06T 2207/30004 (2013.01)] | 10 Claims |
1. A computer-implemented method for training a computer-implemented learning method to identify a genus or species of a parasite ovum in a sample, the method comprising:
accessing a plurality of computer-readable training images, the training images being obtained by light microscopy of one or more samples containing a parasite ovum and a non-parasite ovum material;
using a first subset of the plurality of computer-readable training images to perform human-supervised machine learning to distinguish images containing a parasite ovum from non-parasite ovum material to provide a machine learning model configured to distinguish parasite ovum material from non-parasite ovum material;
using a second subset of the plurality of computer-readable training images to perform frame cropping, the frame cropping comprising identifying a parasite ovum and cropping one or more of the plurality of computer-readable training images so as to produce one or more cropped computer-readable images, each of the one or more cropped computer-readable images showing predominantly the parasite ovum;
by human means identifying the genus or species of the parasite ovum in each of the one or more cropped computer-readable images where identification is possible;
associating an identification label with each of the one or more cropped computer-readable images where identification was possible;
applying a computer-implemented deep learning network feature extraction method to each labelled cropped computer-readable image; and
applying the machine learning model to each cropped computer-readable image to determine if the cropped computer-readable image contains a parasite ovum,
wherein the computer-implemented learning method is configured to associate one or more features extracted by the feature extraction method with a parasite ovum.
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