CCS-GAN: COVID-19 CT-scan classification with very few positive training images

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2110.01605.pdf (1.39 MB)

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https://link.springer.com/article/10.1007/s10278-023-00811-2

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https://doi.org/10.1007/s10278-023-00811-2
 http://hdl.handle.net/11603/25790

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UMBC Computer Science and Electrical Engineering Department
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https://orcid.org/0000-0003-1114-4248
 https://orcid.org/0000-0002-8632-6292

Date

2023-04-17

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journal articles
preprints
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Citation of Original Publication

Menon, Sumeet, Jayalakshmi Mangalagiri, Josh Galita, Michael Morris, Babak Saboury, Yaacov Yesha, Yelena Yesha, Phuong Nguyen, Aryya Gangopadhyay, and David Chapman. “CCS-GAN: COVID-19 CT Scan Generation and Classification with Very Few Positive Training Images.” Journal of Digital Imaging 36, no. 4 (August 1, 2023): 1376–89. https://doi.org/10.1007/s10278-023-00811-2.

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Abstract

We present a novel algorithm that is able to classify COVID-19 pneumonia from CT Scan slices using a very small sample of training images exhibiting COVID19 pneumonia in tandem with a larger number of normal images. This algorithm is able to achieve high classification accuracy using as few as 10 positive training slices (from 10 positive cases), which to the best of our knowledge is one order of magnitude fewer than the next closest published work at the time of writing. Deep learning with extremely small positive training volumes is a very difficult problem and has been an important topic during the COVID19 pandemic, because for quite some time it was difficult to obtain large volumes of COVID-19 positive images for training. Algorithms that can learn to screen for diseases using few examples are an important area of research. We present the Cycle Consistent Segmentation Generative Adversarial Network (CCS-GAN). CCS-GAN combines style transfer with pulmonary segmentation and relevant transfer learning from negative images in order to create a larger volume of synthetic positive images for the purposes of improving diagnostic classification performance. The performance of a VGG-19 classifier plus CCS-GAN was trained using a small sample of positive image slices ranging from at most 50 down to as few as 10 COVID-19 positive CT-scan images. CCS-GAN achieves high accuracy with few positive images and thereby greatly reduces the barrier of acquiring large training volumes in order to train a diagnostic classifier for COVID-19.