Abstract
Lung disease is a kind of infection that cannot be observed by human eyes, but it can be detected by X-ray images. Nevertheless, diagnostic accuracy from the look on these X-ray images is always a challenging task because it frequently relies on the experiences of the doctors. Hence, many computer vision techniques are developed to support medical staffs to perform lung disease detection. So as to encourage different detectors to identify different kinds of lung disease, this paper proposes a novel post-processing technique, a Weighted Box Fusion (WBF) ensembling algorithm, which may improve the efficiency in lung disease detection. This algorithm is applied to an available benchmarking dataset of X-ray chest in literature for justifying its performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Centers for Disease Control and Prevention. https://www.cdc.gov/dotw/pneumonia/
Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K, Lungren MP (2017) Radiologist‐level pneumonia detection on chest X‐rays with deep learning. arXiv preprint arXiv:1711.05225
Lakhani P, Sundaram B (2017) Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2):574–582
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
Islam MT, Aowal MA, Minhaz AT, Ashraf K (2017) Abnormality detection and localization in chest X-rays using deep convolutional neural networks. Computer Science, Cornell University, arXiv:1705.09850v3 [cs.CV]
Yao L, Poblenz E, Dagunts D, Covington B, Bernard D, Lyman K (2017) Learning to diagnose from scratch by exploiting dependencies among labels. Computer Science, Cornell University, arXiv:1710.10501v2 [cs.CV]
Li Z, Wang C, Han M, Xue Y, Wei W, Li LJ, Fei-Fei L (2018) Thoracic disease identification and localization with limited supervision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8290–8299
Cai J, Lu L, Harrison AP, Shi X, Chen P, Yang L (2018) Iterative attention mining for weakly supervised thoracic disease pattern localization in chest X-rays. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 589–598
Solovyev R, Wang W (2019) Weighted boxes fusion: ensembling boxes for object detection models. Image Vis Comput 107, Article 104117
Bodla N, Singh B, Chellappa R, Davis LS (2017) Soft-NMS—improving object detection with one line of code. In: Proceedings of the IEEE international conference on computer vision, pp 5561–5569
Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM (2017) ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2097–2106
Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921–2929
Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A (2019) PyTorch: an imperative style, high-performance deep learning library. In: Advances in neural information processing systems, pp 8024–8035
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. 3rd International Conference on Learning Representations (ICLR) (Poster section), arXiv:1412.6980v9 [cs.LG]
Zhou H, Li Z, Ning C, Tang J (2017) CAD: Scale invariant framework for real-time object detection. In: Proceedings of the IEEE international conference on computer vision workshops, pp 760–68
Conflicts of Interest
The authors have no conflict of interest to declare.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Tran, H., TonThat, L., Trang, K. (2022). Weighted Box Fusion Ensembling for Lung Disease Detection. In: Van Toi, V., Nguyen, TH., Long, V.B., Huong, H.T.T. (eds) 8th International Conference on the Development of Biomedical Engineering in Vietnam. BME 2020. IFMBE Proceedings, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-030-75506-5_60
Download citation
DOI: https://doi.org/10.1007/978-3-030-75506-5_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75505-8
Online ISBN: 978-3-030-75506-5
eBook Packages: EngineeringEngineering (R0)