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2021 | OriginalPaper | Chapter

Ultrasound Image Classification Using ACGAN with Small Training Dataset

Authors : Sudipan Saha, Nasrullah Sheikh

Published in: Recent Trends in Signal and Image Processing

Publisher: Springer Nature Singapore

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Abstract

B-mode ultrasound imaging is a popular medical imaging technique. Like other image processing tasks, deep learning has been used for analysis of B-mode ultrasound images in the last few years. However, training deep learning models require large labeled datasets, which is often unavailable for ultrasound images. The lack of large labeled data is a bottleneck for the use of deep learning in ultrasound image analysis. To overcome this challenge, in this work, we exploit auxiliary classifier generative adversarial network (ACGAN) that combines the benefits of data augmentation and transfer learning in the same framework. We conduct experiment on a dataset of breast ultrasound images that shows the effectiveness of the proposed approach.

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Metadata
Title
Ultrasound Image Classification Using ACGAN with Small Training Dataset
Authors
Sudipan Saha
Nasrullah Sheikh
Copyright Year
2021
Publisher
Springer Nature Singapore
Book
Recent Trends in Signal and Image Processing

Print ISBN: 978-981-336-965-8

Electronic ISBN: 978-981-336-966-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-33-6966-5_9