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:
Dec. 09, 2025

Filed:

Aug. 12, 2025
Applicant:

Prince Mohammad Bin Fahd University, Dhahran, SA;

Inventors:

Muhammad Attique Khan, Dhahran, SA;

Khaled S. Fawagreh, Dhahran, SA;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); A61B 5/00 (2006.01); A61B 5/055 (2006.01); G06N 3/02 (2006.01); G06N 3/045 (2023.01); G06N 3/0464 (2023.01); G06N 5/045 (2023.01); G06N 20/00 (2019.01); G06V 10/50 (2022.01); G06V 10/70 (2022.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01); G06V 30/19 (2022.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 5/0042 (2013.01); A61B 5/055 (2013.01); A61B 5/4082 (2013.01); A61B 5/7264 (2013.01); G06N 3/02 (2013.01); G06N 3/045 (2023.01); G06N 3/0464 (2023.01); G06N 5/045 (2013.01); G06N 20/00 (2019.01); G06V 10/50 (2022.01); G06V 10/70 (2022.01); G06V 10/764 (2022.01); G06V 10/765 (2022.01); G06V 10/7715 (2022.01); G06V 10/809 (2022.01); G06V 10/82 (2022.01); G06V 20/70 (2022.01); G06V 30/19173 (2022.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01); A61B 2576/026 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30096 (2013.01); G06T 2219/004 (2013.01); G06V 2201/03 (2022.01);
Abstract

A computer-implemented system, and method for classifying Parkinson's Disease (PD) from magnetic resonance imaging (MRI) data. The method includes receiving an MRI image. The method includes processing the MRI image through a C3BAM-Net convolutional neural network (CNN) architecture to obtain a plurality of attention-enhanced feature maps. The method includes classifying the input MRI image into one of a plurality of the PD categories based on the plurality of attention-enhanced feature maps. Where the C3BAM-Net CNN includes a plurality of convolutional layers, a plurality of Rectified Linear Unit (ReLU) activations, a plurality of max pooling layers, a plurality of Convolutional Block Attention Modules (CBAMs), a flattening layer, and a plurality of dense layers. The method includes each CBAM of the plurality of CBAMs includes a Channel Attention Module (CAM) and a Spatial Attention Module (SAM) arranged sequentially.


Find Patent Forward Citations

Loading…