Enhancing Small Medical Dataset Classification Performance Using GAN: Informatics

Mohammad Alauthman, Ahmad Al-qerem, Bilal Sowan, Ayoub Alsarhan, Mohammed Eshtay, Amjad Aldweesh*, Nauman Aslam

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
55 Downloads (Pure)


Developing an effective classification model in the medical field is challenging due to limited datasets. To address this issue, this study proposes using a generative adversarial network (GAN) as a data-augmentation technique. The research aims to enhance the classifier’s generalization performance, stability, and precision through the generation of synthetic data that closely resemble real data. We employed feature selection and applied five classification algorithms to thirteen benchmark medical datasets, augmented using the least-square GAN (LS-GAN). Evaluation of the generated samples using different ratios of augmented data showed that the support vector machine model outperforms other methods with larger samples. The proposed data augmentation approach using a GAN presents a promising solution for enhancing the performance of classification models in the healthcare field.
Original languageEnglish
Article number28
Number of pages20
Issue number1
Publication statusPublished - 8 Mar 2023

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