Optical Technique, Volume. 49, Issue 1, 120(2023)
A transfer network for pulmonary tuberculosis lesions detection on the fusion of image features of pneumonia
[1] [1] World Health Organization. Global tuberculosis report 2021[EB/OL]. (2021-10-14)/[2022-08-01]. https:∥www.who.int/publications/i/item/9789240037021.
[7] [7] An L, Peng K X, Yang X, et al. E-TBNet: Light deep neural network for automatic detection of tuberculosis with x-ray dr imaging[J]. Sensors,2022,22(3):821.
[8] [8] Munadi K, Muchtar K, Maulina N, et al. Image enhancement for tuberculosis detection using deep learning[J]. IEEE Access,2020,8:217897-217907.
[9] [9] Rahman T, Khandakar A, Kadir M A, et al. Reliable tuberculosis detection using chest x-ray with deep learning, segmentation and visualization[J]. IEEE Access,2020,8:191586-191601.
[11] [11] Rajaraman S, Antani S K. Modality-specific deep learning model ensembles toward improving TB detection in chest radiographs[J]. IEEE Access,2020,8:27318-27326.
[12] [12] World Health Organization. Updated WHO Information Note: Ensuring continuity of TB services during the COVID-19 pandemic[EB/OL]. (2021-05-05)/[2022-08-01]. https:∥www.who.int/news/item/05-05-2021-updated-who-information-note-ensuring-continuity-of-tb-services-during-the-covid-19-pandemic.
[14] [14] Weiss K, Khoshgoftaar T M, Wang D D. A survey of transfer learning[J]. Journal of Big Data,2016,3(1):1-40.
[15] [15] Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16×16 words: transformers for image recognition at scale[EB/OL].(2020-10-01)/[2022-08-01].https:∥arxiv.org/abs/2010.11929.
[16] [16] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916.
[17] [17] Wang Q, Wu B, Zhu P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]∥Proceedings of The 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.New York,USA:IEEE,2020:11531-11539.
[18] [18] Liu Y, Wu Y H, Ban Y, et al. Rethinking computer-aided tuberculosis diagnosis[C]∥Proceedings of The 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York, USA:IEEE,2020:2646-2655.
[19] [19] The Society for Imaging Informatics in Medicine. The 2021 SIIM-FISABIO-RSNA machine learning COVID-19 challenge: Annotation and standard exam classification of COVID-19 chest radiographs[EB/OL]. (2021-05-17)/[2022-10-01]. https:∥www.kaggle.com/competitions/siim-covid19-detection.
[20] [20] Liu W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector[C]∥Proceedings of the European Conference on Computer Vision.Berlin,Germany:Springer-Verlag,2016:21-37.
[21] [21] Redmon J, Farhadi A. YOLOv3: An incremental improvement[EB/OL]. (2018-04-08)/[2022-08-01]. https:∥arxiv.org/abs/1804.02767.
[22] [22] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]∥Proceedings of The IEEE International Conference on Computer Vision.New York,USA:IEEE,2017:2980-2988.
[23] [23] Ren S Q, He K M, Girshick R, et al. Faster r-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149.
[24] [24] Li C, Li L, Jiang H, et al. YOLOv6:A single-stage object detection framework for industrial applications[EB/OL]. (2022-09-17)/[2022-10-01]. https:∥arxiv.org/abs/2209.02976.
[25] [25] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[EB/OL]. (2022-07-06)/[2022-10-01]. https:∥arxiv.org/abs/2207.02696.
Get Citation
Copy Citation Text
AN Le, PENG Kexin, YANG Xing, HUANG Pan, WEI Biao, FENG Peng. A transfer network for pulmonary tuberculosis lesions detection on the fusion of image features of pneumonia[J]. Optical Technique, 2023, 49(1): 120
Category:
Received: Aug. 17, 2022
Accepted: --
Published Online: Mar. 19, 2023
The Author Email: PENG Kexin (pkx@cdut.edu.cn)
CSTR:32186.14.