Detecting Cognitive Decline Using a Novel Doodle-Based Neural Network

Pearson, Connor, De La Iglesia, Beatriz ORCID: https://orcid.org/0000-0003-2675-5826 and Sami, Saber (2022) Detecting Cognitive Decline Using a Novel Doodle-Based Neural Network. In: 2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings. 2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings . The Institute of Electrical and Electronics Engineers (IEEE), ITA, pp. 99-103. ISBN 9781665485746

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Abstract

A key part in the diagnosis of cognitive decline are visuospatial based tests. These visuospatial tests often involve a form of drawing task. In this paper, we build an automated multiclass classifier to assign hand-drawn doodles from Google’s online game Quick, Draw! into 24 unique categories that are simple to draw doodles. Our goal is to create a prototype of an automated online diagnosis tool that resembles the visuospatial portion of established pen and paper cognitive examinations. We built a CNN using the Tensor Flow Keras API, and tested multiple iterations of each model neuron structure. We created a web interface able to capture user inputs from a browser window as they draw the requested doodle for each test stage. The images are relayed back to a server and processed through the same model trained on the Google QuickDraw! dataset to determine a patient’s score. Herein we use these model predictions as a measurement of the users drawing skills. Using a CNN based neural network we achieved a 90.46% model accuracy and around 70% implementation accuracy which is not dissimilar to human pen and paper ratings.

Item Type:	Book Section
Additional Information:	Publisher Copyright: © 2022 IEEE.
Uncontrolled Keywords:	artificial intelligence,computer science applications,media technology,neuroscience (miscellaneous),instrumentation ,/dk/atira/pure/subjectarea/asjc/1700/1702
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Medicine and Health Sciences > Norwich Medical School
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	07 Nov 2022 12:30
Last Modified:	18 Apr 2023 01:05
URI:	https://ueaeprints.uea.ac.uk/id/eprint/89683
DOI:	10.1109/MetroXRAINE54828.2022.9967549

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