Lung nodule classification using deep feature fusion in chest radiography.
作者： Changmiao Wang , Ahmed Elazab , Jianhuang Wu , Qingmao Hu
DOI: 10.1016/J.COMPMEDIMAG.2016.11.004
关键词:
摘要: Lung nodules are small, round, or oval-shaped masses of tissue in the lung region. Early diagnosis and treatment can significantly improve quality patients' lives. Because their small size interlaced nature chest anatomy, detection using different medical imaging techniques becomes challenging. Recently, several methods for computer aided (CAD) were proposed to with good performances. However, current unable achieve high sensitivity specificity. In this paper, we propose deep feature fusion from non-medical training hand-crafted features reduce false positive results. Based on our experimentation public dataset, results show that, promising terms specificity (69.3% 96.2%) at 1.19 per image, which is better than single (62% 95.4%) 1.45 image. As it stands, that used classify candidate have resulted a more outcome as compared learning features. This will CAD method based use effectively diagnose presence nodules.
sci-hub.se 参考文章(55)
Dan C. Cireşan, Alessandro Giusti, Luca M. Gambardella, Jürgen Schmidhuber, Mitosis detection in breast cancer histology images with deep neural networks. medical image computing and computer-assisted intervention. ,vol. 16, pp. 411- 418 ,(2013) , 10.1007/978-3-642-40763-5_51
Rongjian Li, Wenlu Zhang, Heung-Il Suk, Li Wang, Jiang Li, Dinggang Shen, Shuiwang Ji, Deep learning based imaging data completion for improved brain disease diagnosis. medical image computing and computer-assisted intervention. ,vol. 17, pp. 305- 312 ,(2014) , 10.1007/978-3-319-10443-0_39
Feng Li, Loc Tran, Kim-Han Thung, Shuiwang Ji, Dinggang Shen, Jiang Li, Robust deep learning for improved classification of AD/MCI patients Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ,vol. 8679, pp. 240- 247 ,(2014) , 10.1007/978-3-319-10581-9_30
Jun Wei, Yoshihiro Hagihara, Akinobu Shimizu, Hidefumi Kobatake, Optimal image feature set for detecting lung nodules on chest X-ray images Springer, Berlin, Heidelberg. pp. 706- 711 ,(2002) , 10.1007/978-3-642-56168-9_118
Heung-Il Suk, , Seong-Whan Lee, Dinggang Shen, Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis. Brain Structure & Function. ,vol. 221, pp. 2569- 2587 ,(2016) , 10.1007/S00429-015-1059-Y
Burçin Buket Oğul, Polat Koşucu, Ahmet Özçam, Sümeyra Demir Kanik, Lung Nodule Detection in X-Ray Images: A New Feature Set IFMBE Proceedings. pp. 150- 155 ,(2015) , 10.1007/978-3-319-11128-5_38
Wei Shen, Mu Zhou, Feng Yang, Caiyun Yang, Jie Tian, Multi-scale Convolutional Neural Networks for Lung Nodule Classification international conference information processing. ,vol. 24, pp. 588- 599 ,(2015) , 10.1007/978-3-319-19992-4_46
Pierre Sermanet, Yann LeCun, David Eigen, Rob Fergus, Michael Mathieu, Xiang Zhang, OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks arXiv: Computer Vision and Pattern Recognition. ,(2013)
Arnold M. R. Schilham, Bram van Ginneken, Marco Loog, Multi-scale Nodule Detection in Chest Radiographs medical image computing and computer assisted intervention. pp. 602- 609 ,(2003) , 10.1007/978-3-540-39899-8_74
Xuechen Li, Suhuai Luo, Qingmao Hu, Jiaming Li, Dadong Wang, Rib suppression in chest radiographs for lung nodule enhancement international conference on information and automation. pp. 50- 55 ,(2015) , 10.1109/ICINFA.2015.7279257