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Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

Bin Nazar, Haris and Moetesum, Momina and Ehsan, Shoaib and Siddiqi, Imran and Khurshid, Khurram and Vincent, Nicole and McDonald-Maier, Klaus D (2017) Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017-11-09 - 2017-11-15, Kyoto, Japan.

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Abstract

Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 21 Jul 2020 09:28
Last Modified: 21 Jul 2020 10:15
URI: http://repository.essex.ac.uk/id/eprint/27625

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