The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 23, 2024

Filed:

Nov. 11, 2019
Applicant:

Ping an Technology (Shenzhen) Co., Ltd., Guangdong, CN;

Inventors:

Rui Wang, Guangdong, CN;

Junhuan Zhao, Guangdong, CN;

Lilong Wang, Guangdong, CN;

Yuanzhi Yuan, Guangdong, CN;

Chuanfeng Lv, Guangdong, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06N 3/045 (2023.01); G06T 7/90 (2017.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06N 3/045 (2023.01); G06T 7/90 (2017.01); G06V 10/82 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30041 (2013.01);
Abstract

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.


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