earticle

논문검색

Bio or medical Information Technology (BIT)

Evaluating Chest Abnormalities Detection: YOLOv7 and Detection Transformer with CycleGAN Data Augmentation

초록

영어

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.

목차

Abstract
1. Introduction
2. Related Work
2.1 You Only Look Once (YOLO)
2.2 Detection Transformer (DETR)
2.3 CycleGAN
2.4 Dataset: VinDr-CSR
3. Experiment Setup and Methodology
3.1 Setup Environment
3.2 Data Augmentation Scenario
3.3 Chest Abnormalities Detection
4. Result and Discussion
5. Conclusion
Acknowledgement
References

저자정보

  • Yoshua Kaleb Purwanto Master Student, Department of Computer Engineering, Dongseo University, Busan, Korea
  • Suk-Ho Lee Professor, Department of Computer Engineering, Dongseo University, Busan, Korea
  • Dae-Ki Kang Professor, Department of Computer Engineering, Dongseo University, Busan, Korea

참고문헌

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 기관로그인 시 무료 이용이 가능합니다.

      • 4,000원

      0개의 논문이 장바구니에 담겼습니다.