Abstract: An image processing system includes an ophthalmoscope device and a processor. The ophthalmoscope device is configured to obtain a color fundus image. The processor is configured to receive the color fundus image; generate a blood vessel segmentation image that corresponds to the color fundus image using a computer vision algorithm or a deep learning model; preprocess the color fundus image and the blood vessel segmentation image to obtain an initial input image; and input the initial input image into a convolutional neural network. The convolutional neural network outputs a value. In addition, the processor generates fundus image analysis information from the cup-to-disc ratio and the value.

Abstract: The disclosure provides an eye state assessment method and an electronic device. The method includes: obtaining an optic disc image area from a first fundus photography and generating multiple optic cup-to-disc ratio assessment results by multiple first models based on the optic disc image area; obtaining a first assessment result of an eye based on the optic cup-to-disc ratio assessment results; performing multiple data augmentation operations on the first fundus photography to generate multiple second fundus photographies; generating multiple retinal nerve fiber layer (RNFL) defect assessment results by multiple second models based on the second fundus photographies; obtaining a second assessment result of the eye based on the RNFL defect assessment results; and obtaining an optic nerve assessment result of the eye based on the first assessment result and the second assessment result.

Abstract: An image processing system includes an ophthalmoscope device and a processor. The ophthalmoscope device is configured to obtain a color fundus image. The processor is configured to receive the color fundus image; generate a blood vessel segmentation image that corresponds to the color fundus image using a computer vision algorithm or a deep learning model; preprocess the color fundus image and the blood vessel segmentation image to obtain an initial input image; and input the initial input image into a convolutional neural network. The convolutional neural network outputs a value. In addition, the processor generates fundus image analysis information from the cup-to-disc ratio and the value.