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DC-GAN-based synthetic X-ray images augmentation for increasing the performance of EfficientNet for COVID-19 detection.
Shah, Pir Masoom; Ullah, Hamid; Ullah, Rahim; Shah, Dilawar; Wang, Yulin; Islam, Saif Ul; Gani, Abdullah; Rodrigues, Joel J P C.
  • Shah PM; School of Computer Science Wuhan University Wuhan China.
  • Ullah H; Department of Computer Science Bacha Khan University Charsadda Pakistan.
  • Ullah R; Department of Computer Science Kohat University of Science and Technology Kohat Pakistan.
  • Shah D; Department of Computer Science University of Malakand Malakand Pakistan.
  • Wang Y; Department of Computer Science Bacha Khan University Charsadda Pakistan.
  • Islam SU; School of Computer Science Wuhan University Wuhan China.
  • Gani A; Department of Computer Science KICSIT, Institute of Space Technology Islamabad Pakistan.
  • Rodrigues JJPC; Faculty of Computing and Informatics University Malaysia Sabah Labuan Malaysia.
Expert Syst ; 39(3): e12823, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1476182
ABSTRACT
Currently, many deep learning models are being used to classify COVID-19 and normal cases from chest X-rays. However, the available data (X-rays) for COVID-19 is limited to train a robust deep-learning model. Researchers have used data augmentation techniques to tackle this issue by increasing the numbers of samples through flipping, translation, and rotation. However, by adopting this strategy, the model compromises for the learning of high-dimensional features for a given problem. Hence, there are high chances of overfitting. In this paper, we used deep-convolutional generative adversarial networks algorithm to address this issue, which generates synthetic images for all the classes (Normal, Pneumonia, and COVID-19). To validate whether the generated images are accurate, we used the k-mean clustering technique with three clusters (Normal, Pneumonia, and COVID-19). We only selected the X-ray images classified in the correct clusters for training. In this way, we formed a synthetic dataset with three classes. The generated dataset was then fed to The EfficientNetB4 for training. The experiments achieved promising results of 95% in terms of area under the curve (AUC). To validate that our network has learned discriminated features associated with lung in the X-rays, we used the Grad-CAM technique to visualize the underlying pattern, which leads the network to its final decision.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Expert Syst Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Expert Syst Year: 2022 Document Type: Article