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Scaling and Cutout Data Augmentation for Cardiac Segmentation

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Proceedings of International Conference on Data Science and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 552))

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Abstract

Convolutional neural network (CNN) has a compelling learning capability, especially on spatial data representation, crucial in dealing with complex learning tasks. However, it requires extensive training data to optimally fit the model, making it susceptible to overfitting problems when the data is scarce, thus limiting its generalization ability. Therefore, it is essential to collect enough data or supplement the dataset with artificial data to improve the performance of the CNN model. In this paper, simple data augmentation through the geometry transformations of scaling and cutting is explored to augment the training dataset for cardiac segmentation. The generated images and labels are combined with the original dataset to double the training data size. Three state-of-the-art semantic segmentation models, which are U-Net, TernausNet, and DabNet, were used to validate the performance improvement of the proposed data augmentation method. The best performance improvement is returned by DabNet with an increment of 0.24% and 5.14% for mean accuracy and mean intersection over union, respectively. Hence, a better segmentation performance will enable medical practitioners to localize the organs effectively and efficiently.

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Acknowledgements

The researchers acknowledge research funds from Universiti Kebangsaan Malaysia through Geran Universiti Penyelidikan (GUP-2019–008) and Ministry of Higher Education Malaysia through Fundamental Research Grant Scheme (FRGS/1/2019/ICT02/UKM/02/1).

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Correspondence to Mohd Asyraf Zulkifley .

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Elizar, E., Zulkifley, M.A., Muharar, R. (2023). Scaling and Cutout Data Augmentation for Cardiac Segmentation. In: Saraswat, M., Chowdhury, C., Kumar Mandal, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 552. Springer, Singapore. https://doi.org/10.1007/978-981-19-6634-7_42

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