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. 21, 2023

Filed:

Mar. 05, 2021
Applicant:

Wipro Limited, Bangalore, IN;

Inventors:

Manjunath Ramachandra Iyer, Bangalore, IN;

Chandrashekar Bangalore Nagaraj, Bangalore, IN;

Shashidhar Soppin, Bangalore, IN;

Assignee:

Wipro Limited, Bangalore, IN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06F 18/24 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G16H 50/20 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G06F 18/213 (2023.01); A61B 6/03 (2006.01);
U.S. Cl.
CPC ...
G06F 18/24 (2023.01); G06F 18/213 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/0016 (2013.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); A61B 6/032 (2013.01); A61B 6/037 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01); G06V 2201/03 (2022.01);
Abstract

System and method for diagnosing a disease using image classification is provided. The method includes receiving diagnostic images associated with a subject. The diagnostic images comprise a first image and a second image captured at a first instance of time and at a second instance of time, respectively. The method further includes extracting feature maps from the first image and the second image, using a first pipeline and a second pipeline of stacked convolutional layers of a temporal convolutional neural network (CNN) model respectively. The method further includes identifying relevant feature maps from the feature maps using temporal CNN layer of the temporal CNN model, generating a concatenated feature map based on the relevant feature maps, determining an image class corresponding to the second image using fully connected layers of the temporal CNN model, and diagnosing the disease associated with the subject based on the image class.


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