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Open AccessArticle

A Novel Virtual Sample Generation Method to Overcome the Small Sample Size Problem in Computer Aided Medical Diagnosing

1
Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo 2007, Australia
2
IRCCS Eugenio Medea, Scientific Institute, 23842 Bosisio Parini, Italy
*
Authors to whom correspondence should be addressed.
Algorithms 2019, 12(8), 160; https://doi.org/10.3390/a12080160
Received: 14 March 2019 / Revised: 18 July 2019 / Accepted: 2 August 2019 / Published: 9 August 2019
(This article belongs to the Special Issue Evolutionary Algorithms in Health Technologies)
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Abstract

Deep neural networks are successful learning tools for building nonlinear models. However, a robust deep learning-based classification model needs a large dataset. Indeed, these models are often unstable when they use small datasets. To solve this issue, which is particularly critical in light of the possible clinical applications of these predictive models, researchers have developed approaches such as virtual sample generation. Virtual sample generation significantly improves learning and classification performance when working with small samples. The main objective of this study is to evaluate the ability of the proposed virtual sample generation to overcome the small sample size problem, which is a feature of the automated detection of a neurodevelopmental disorder, namely autism spectrum disorder. Results show that our method enhances diagnostic accuracy from 84%–95% using virtual samples generated on the basis of five actual clinical samples. The present findings show the feasibility of using the proposed technique to improve classification performance even in cases of clinical samples of limited size. Accounting for concerns in relation to small sample sizes, our technique represents a meaningful step forward in terms of pattern recognition methodology, particularly when it is applied to diagnostic classifications of neurodevelopmental disorders. Besides, the proposed technique has been tested with other available benchmark datasets. The experimental outcomes showed that the accuracy of the classification that used virtual samples was superior to the one that used original training data without virtual samples. View Full-Text
Keywords: small sample issue; deep learning; autism; virtual sample generation; small dataset; large dataset; normal Gaussian distribution; mega trend diffusion; functional virtual population; multivariate normal synthetic small sample issue; deep learning; autism; virtual sample generation; small dataset; large dataset; normal Gaussian distribution; mega trend diffusion; functional virtual population; multivariate normal synthetic
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wedyan, M.; Crippa, A.; Al-Jumaily, A. A Novel Virtual Sample Generation Method to Overcome the Small Sample Size Problem in Computer Aided Medical Diagnosing. Algorithms 2019, 12, 160.

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