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:
Nov. 07, 2023

Filed:

Aug. 05, 2021
Applicant:

National Chung Cheng University, Chiayi County, TW;

Inventors:

Hsiang-Chen Wang, Chiayi, TW;

Tsung-Yu Yang, Chiayi County, TW;

Yu-Sheng Chi, Chiayi County, TW;

Ting-Chun Men, Chiayi County, TW;

Assignee:

National Chung Cheng University, Chiayi County, TW;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 3/40 (2006.01); G06T 7/00 (2017.01); G06T 7/73 (2017.01); G06V 10/75 (2022.01); G06V 10/82 (2022.01); G06V 30/146 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0014 (2013.01); G06T 3/40 (2013.01); G06V 10/754 (2022.01); G06V 10/76 (2022.01); G06T 2207/10036 (2013.01); G06T 2207/30096 (2013.01);
Abstract

This application provides a method for detecting images of testing object using hyperspectral imaging. Firstly, obtaining a hyperspectral imaging information according to a reference image, hereby, obtaining corresponded hyperspectral image from an input image and obtaining corresponded feature values for operating Principal components analysis to simplify feature values. Then, obtaining feature images by Convolution kernel, and then positioning an image of an object under detected by a default box and a boundary box from the feature image. By Comparing with the esophageal cancer sample image, the image of the object under detected is classifying to an esophageal cancer image or a non-esophageal cancer image. Thus, detecting an input image from the image capturing device by the convolutional neural network to judge if the input image is the esophageal cancer image for helping the doctor to interpret the image of the object under detected.


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