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Image Augmentation Techniques for Mammogram Analysis.

Oza, P., Sharma, P., Patel, S., Adedoyin, F. and Bruno, A., 2022. Image Augmentation Techniques for Mammogram Analysis. Journal of Imaging, 8 (5), 141.

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DOI: 10.3390/jimaging8050141


Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The study aims to provide insights into augmentation and deep learning-based augmentation techniques.

Item Type:Article
Additional Information:This article belongs to the Special Issue Advances in IoMT, Deep Learning and Computer Vision for Mammographic Image Analysis
Uncontrolled Keywords:Data Augmentation, Deep Learning, Medical Imaging, Mammograms
Group:Faculty of Science & Technology
ID Code:36908
Deposited By: Symplectic RT2
Deposited On:03 May 2022 14:10
Last Modified:24 May 2022 13:20


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