| CPC G06V 40/193 (2022.01) [G06T 7/0012 (2013.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G16H 50/30 (2018.01); G06T 2207/30041 (2013.01); G06T 2207/30101 (2013.01); G06V 2201/03 (2022.01); G06V 2201/07 (2022.01)] | 13 Claims |
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1. A computing device comprising:
one or more processors;
a memory; and
one or more programs, wherein
the one or more programs are stored in the memory and configured to be executed by the one or more processors,
the one or more programs include:
an instruction for acquiring a photographed image obtained by photographing a patient's eyeball,
an instruction for preprocessing the photographed image, generating a blood vessel image from the pre-processed photographed image using machine learning-based technology, and providing necessary information for a diagnosis of Alzheimer's disease based on the generated blood vessel image, and
an instruction for generating diagnostic prediction information based on the necessary information for a diagnosis of Alzheimer's disease,
wherein the instruction for providing the necessary information for the diagnosis of Alzheimer's disease includes:
an instruction for generating a data set by extracting a retinal region from the photographed acquired image, and
an instruction for classifying a patient's risk rating for Alzheimer's disease from the data set by receiving the data set using a machine learning module,
wherein the instruction for classifying the patient's risk rating for Alzheimer's disease includes:
an instruction for generating a feature map from a retinal image included in the data set by receiving the data set using an encoder module,
an instruction for generating the blood vessel image based on the feature map by receiving the feature map using a decoder module,
an instruction for generating a final blood vessel image based on the retinal image, the feature map, and the blood vessel image by receiving the retinal image, the feature map, and the blood vessel image using a generation module, and
an instruction for performing category classification to provide the necessary information for the diagnosis of Alzheimer's disease based on the final blood vessel image by receiving the final blood vessel image using a classification module,
wherein the instruction for classifying the patient's risk rating for Alzheimer's disease further includes:
an instruction for generating a feature map from a retinal image included in the data set by receiving the data set using an encoder module,
an instruction for generating the blood vessel image based on the feature map by receiving the feature map using a decoder module,
an instruction for generating a final blood vessel image based on the retinal image, the feature map, and the blood vessel image by receiving the retinal image, the feature map, and the blood vessel image using a generation module, and
an instruction for performing category classification to provide the necessary information for the diagnosis of Alzheimer's disease based on the final blood vessel image by receiving the final blood vessel image using a classification module,
wherein the generation module is trained to:
divide the feature map and the blood vessel image into the same size, respectively, by receiving the retinal image, the feature map, and the blood vessel image, generate a similarity distribution by comparing similarities between any one of a plurality of the divided blood vessel images and a plurality of the divided feature maps, respectively, reflect the similarity distribution in the retinal image, and extract a blood vessel from a region corresponding to any one of the plurality of divided blood vessel images from the retinal image using the retinal image in which the similarity distribution is reflected and any one of the plurality of divided blood vessel images.
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