Patents by Inventor Shaolin LV

Shaolin LV has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Method and system for classifying diabetic retina images based on deep learning
    Patent number: 11132799
    Abstract: A method for classifying diabetic retina images based on deep learning includes: obtaining a fundus image; importing the same fundus image into a microhemangioma lesion recognition model, a hemorrhage lesion recognition model and an exudation lesion recognition model for recognition; extracting lesion feature information from the recognition results, and then using a trained support vector machine classifier to classify the extracted lesion feature information to obtain a classification result. The microhemangioma lesion recognition model is obtained by extracting a candidate microhemangioma lesion region in the fundus image and inputting it into a CNN model for training; the hemorrhage lesion recognition model and the exudation lesion recognition model are obtained by labeling a region in the fundus image as a hemorrhage lesion region and an exudation lesion region, and then inputting the result into an FCN model for training. A system for the same is also disclosed.
    Type: Grant
    Filed: March 30, 2020
    Date of Patent: September 28, 2021
    Assignee: BOZHON PRECISION INDUSTRY TECHNOLOGY CO., LTD.
    Inventors: Shaolin Lv, Jiangbing Zhu, Qian Wang, Ruixia Chen
  • METHOD AND SYSTEM FOR CLASSIFYING DIABETIC RETINA IMAGES BASED ON DEEP LEARNING
    Publication number: 20200234445
    Abstract: A method for classifying diabetic retina images based on deep learning includes: obtaining a fundus image; importing the same fundus image into a microhemangioma lesion recognition model, a hemorrhage lesion recognition model and an exudation lesion recognition model for recognition; extracting lesion feature information from the recognition results, and then using a trained support vector machine classifier to classify the extracted lesion feature information to obtain a classification result. The microhemangioma lesion recognition model is obtained by extracting a candidate microhemangioma lesion region in the fundus image and inputting it into a CNN model for training; the hemorrhage lesion recognition model and the exudation lesion recognition model are obtained by labeling a region in the fundus image as a hemorrhage lesion region and an exudation lesion region, and then inputting the result into an FCN model for training. A system for the same is also disclosed.
    Type: Application
    Filed: March 30, 2020
    Publication date: July 23, 2020
    Inventors: Shaolin LV, Jiangbing ZHU, Qian WANG, Ruixia CHEN