Efficient Convolutional Neural Networks for Automated Cognitive Diagnosis

Pearson, Connor, De la Iglesia, Beatriz ORCID: https://orcid.org/0000-0003-2675-5826 and Sami, Saber (2024) Efficient Convolutional Neural Networks for Automated Cognitive Diagnosis. In: 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), 2024-10-21 - 2024-10-23, St Albans, United Kingdom.

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Abstract

Digitization has transformed diagnostic methods in several healthcare sectors. The standard cognitive assessment tests, evaluate cognitive impairment including early stages that can potentially progress to Alzheimer's Disease. However, it poses challenges due to manual administration. Here we propose using a novel convolutional neural network described here as CogniNet and compare its performance with leading doodle recognition transfer learning models to automate the visuospatial aspect of cognitive tests. Based on our CogniNet model we developed a web-application on the Laravel framework with enhanced accessibility and security features. Our convolutional neural network achieved 91.5% accuracy, while the EfficientNet And MobileNet transfer learning models reached 87.5% and 85.5% respectively.

Item Type:	Conference or Workshop Item (Paper)
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Medicine and Health Sciences > Norwich Medical School
Depositing User:	LivePure Connector
Date Deposited:	21 Jan 2025 00:32
Last Modified:	22 Jan 2025 00:32
URI:	https://ueaeprints.uea.ac.uk/id/eprint/98269
DOI:	10.1109/MetroXRAINE62247.2024.10796848

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