Ping an Technology (shenzhen) Co., Ltd.

Lilong Wang

Rui Wang

Ge Li

Peng Gao

Meng Zhang

Yijun Tang

Chuanfeng Lv

Dongyi Fan

Guanzheng Wang

Yuanzhi Yuan

Junhuan Zhao

A fundus color photo image grading method and apparatus, a computer device, and a storage medium are provided. The method comprises: obtaining an original image, enhancing the original image, and obtaining a target image (S); performing color processing on the original image and the target image, and respectively obtaining a first processed image and a second processed image (S); using a pre-trained grading model to perform processing on the first processed image and the second processed image, and obtaining a target grading result (S). Multi-color space first and second processed images act as model inputs, and prediction is performed by means of fused features on a whole image scale to implement the classification and grading of common fundus color photo diseases, so as to automatically screen fundus color photos with pathological changes, achieve a pre-screening effect, and improve operational efficiency.

Fundus color photo image grading method and apparatus, computer device, and storage medium

The application relates to the technical field of artificial intelligence, and provides a method, device, electronic equipment and storage medium for positioning macular center in fundus images. The method comprises: acquiring a detection result of the fundus image detection model, wherein the detection result includes an optic disc area, and a first detection block and a first confidence score corresponding to the optic disc area, and a macular area, and a second detection block and a second confidence score corresponding to the macular area; calculating a center point coordinate of the optic disc area according to the first detection block, and calculating a center point coordinate of the macular area according to the second detection block; identifying whether the to-be-detected fundus image is a left eye fundus image or a right eye fundus image, and correcting a center point of the macular area using different correction models.

Method, device, electronic equipment and storage medium for positioning macular center in fundus images

An image enhancement processing method includes: acquiring an initial image, preprocessing the initial image, and acquiring an original feature image containing a target feature; performing an edge detection on the original feature image using an edge detection algorithm to obtain an original gradient image, obtaining a statistics ring based on the original feature image, and performing an iterative process on the statistics ring; obtaining a to-be-processed image based on an inner diameter of on the statistics ring, and determining to-be-processed parameters of the to-be-processed image: acquiring a standard image corresponding to the target feature, determining a standard area corresponding to the standard image, and acquiring standard image parameters corresponding to the standard area; performing a migration process on the to-be-processed image to obtain a migration image; and performing a restricted contrast adaptive histogram equalization process on the migration image to obtain a target enhanced image.

Image enhancement processing method, device, equipment, and medium based on artificial intelligence

The present disclosure provides a method, device, computer apparatus, and storage medium for training recognition model and recognizing fundus features. The method includes: obtaining a color fundus image sample associated with a label value, inputting the color fundus image sample into a preset recognition model containing initial parameters; extracting a red channel image; inputting the red channel image into the first convolutional neural network to obtain a first recognition result and a feature image of the red channel image; combining the color fundus image sample with the feature image to generate a combined image, and inputting the combined image into the second convolutional neural network to obtain a second recognition result; obtaining a total loss value through a loss function, and when the total loss value is less than or equal to a preset loss threshold, ending the training of the preset recognition model.

Method, device, apparatus, and medium for training recognition model and recognizing fundus features

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Guangdong, China

Lilong Wang