Modeling method and apparatus for diagnosing ophthalmic disease based on artificial intelligence, and storage medium

A modeling apparatus for diagnosing an ophthalmic disease based on artificial intelligence, comprising:

• a processor;
• and
• a memory, configured to store executable program codes by the processor;
• wherein the processor is configured to operate a program corresponding to the executable program codes by reading the executable program codes stored in the memory to perform:
• establishing a data collection of ophthalmic images and a data collection of non-image ophthalmic disease diagnosis questionnaires;
• training a first neural network model by employing the data collection of the ophthalmic images to obtain a first classification model;
• training a second classification model by employing the data collection of non-image ophthalmic disease diagnosis questionnaires;
• and
• merging the first classification model and the second classification model to obtain a target classification network model, wherein a test result outputted by the target classification network model is used as a diagnosis result of the ophthalmic disease,
• wherein the processor is further configured to perform at least one of:
• training the first neural network model by employing the data collection of ophthalmic images, in which, the first neural network model comprises:
• at least two convolution layers, an activation function, at least two full connection layers, and a sigmoid activation function, and each convolution layer is connected to a pooling layer;
• and
• migrating an existing neural network model trained based on massive public data collections to a supervised learning model, and taking the supervised learning model as the first classification model.

A non-transitory computer readable storage medium having instructions stored thereon, wherein when the instruction is executed by a processor of an electronic device, the processor is configured to execute a modeling method for diagnosing the ophthalmic disease based on artificial intelligence, and the method comprises:

• establishing a data collection of ophthalmic images and a data collection of non-image ophthalmic disease diagnosis questionnaires;
• training a first neural network model by employing the data collection of the ophthalmic images to obtain a first classification model;
• training a second classification model by employing the data collection of non-image ophthalmic disease diagnosis questionnaires;
• and
• merging the first classification model and the second classification model to obtain a target classification network model, wherein a test result outputted by the target classification network model is used as a diagnosis result of the ophthalmic disease,
• wherein training the first neural network model by employing the data collection of the ophthalmic images to obtain the first classification model comprises at least one of:
• training the first neural network model by employing the data collection of ophthalmic images, in which, the first neural network model comprises:
• at least two convolution layers, an activation function, at least two full connection layers, and a sigmoid activation function, and each convolution layer is connected to a pooling layer;
• and
• migrating an existing neural network model trained based on massive public data collections to a supervised learning model, and taking the supervised learning model as the first classification model.