Patent ID: 11934933
Assignee: RETINA-AL HEALTH, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G  A | IPC A  G

Claim 0:
1. A method for weighted-ensemble training of machine-learning models to classify ophthalmic images according to features such as disease type and state; where the method comprises of:
a) an ensemble of machine-learning models each of which consists of:
i. a feature extraction mechanism; and
ii. a classification mechanism;

b) a step to split the input data into training and test sets;
c) a step to initialize the weights;
d) for each model, a step in which the feature extraction mechanism yields a feature vector or other object encoding the ophthalmic image features;
e) for each model, a step in which the feature vector is passed into the classification mechanism to yield a class prediction;
f) for each model, a mechanism to iteratively update the weights to reduce class prediction error;
g) for each model, a stopping mechanism for the iteration;
h) a step to compare and rank the models based on their performance on a test dataset:
i) a step to assign weights to the various models in the ensemble; and
j) given a subject ophthalmic image, a step to compute the weighted-average of the class predictions of the plurality of models, and to choose the ophthalmic image class based on this weighted-averaging step.