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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.
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