International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Evaluating Chest Abnormalities Detection: YOLOv7 and Detection Transformer with CycleGAN Data Augmentation

  • Yoshua Kaleb Purwanto (Department of Computer Engineering, Dongseo University) ;
  • Suk-Ho Lee (Department of Computer Engineering, Dongseo University) ;
  • Dae-Ki Kang (Department of Computer Engineering, Dongseo University)
  • Received : 2024.05.06
  • Accepted : 2024.05.21
  • Published : 2024.06.30
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Abstract

In this paper, we investigate the comparative performance of two leading object detection architectures, YOLOv7 and Detection Transformer (DETR), across varying levels of data augmentation using CycleGAN. Our experiments focus on chest scan images within the context of biomedical informatics, specifically targeting the detection of abnormalities. The study reveals that YOLOv7 consistently outperforms DETR across all levels of augmented data, maintaining better performance even with 75% augmented data. Additionally, YOLOv7 demonstrates significantly faster convergence, requiring approximately 30 epochs compared to DETR's 300 epochs. These findings underscore the superiority of YOLOv7 for object detection tasks, especially in scenarios with limited data and when rapid convergence is essential. Our results provide valuable insights for researchers and practitioners in the field of computer vision, highlighting the effectiveness of YOLOv7 and the importance of data augmentation in improving model performance and efficiency.

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Acknowledgement

This work was supported by Dongseo University, "Dongseo Cluster Project" Research Fund of 2023 (DSU-20230004).

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