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. 2023 Jan 12:1-15.
doi: 10.1007/s12530-023-09484-2. Online ahead of print.

AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19

Hadi Alhares  1 , Jafar Tanha  1 , Mohammad Ali Balafar  1
Affiliations

AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19

Hadi Alhares et al. Evol Syst (Berl). .

Abstract

In recent years, deep learning techniques have been widely used to diagnose diseases. However, in some tasks, such as the diagnosis of COVID-19 disease, due to insufficient data, the model is not properly trained and as a result, the generalizability of the model decreases. For example, if the model is trained on a CT scan dataset and tested on another CT scan dataset, it predicts near-random results. To address this, data from several different sources can be combined using transfer learning, taking into account the intrinsic and natural differences in existing datasets obtained with different medical imaging tools and approaches. In this paper, to improve the transfer learning technique and better generalizability between multiple data sources, we propose a multi-source adversarial transfer learning model, namely AMTLDC. In AMTLDC, representations are learned that are similar among the sources. In other words, extracted representations are general and not dependent on the particular dataset domain. We apply the AMTLDC to predict Covid-19 from medical images using a convolutional neural network. We show that accuracy can be improved using the AMTLDC framework, and surpass the results of current successful transfer learning approaches. In particular, we show that the AMTLDC works well when using different dataset domains, or when there is insufficient data.

Keywords: COVID-19 diagnosis; Coronavirus pneumonia; Deep learning; Diagnose diseases; Multi-source adversarial domain adaptation.

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Conflict of interest statement

Conflict of interestThe authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.

Figures

Fig. 1
Fig. 1
AMTLDC framework
Fig. 2
Fig. 2
Some images after preprocessing step
Fig. 3
Fig. 3
CNN-based Feature Extractor, Classification, and Discrimination Blocks
Fig. 4
Fig. 4
Efficient adversarial training approach in a multi-source transfer learning environment
Fig. 5
Fig. 5
Confusion matrix of evaluation on the test set of the SARS-CoV-2 dataset
See this image and copyright information in PMC

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