Abstract
The development of AI has made some major advances in recent years and its potential appears to be promising. In the healthcare sector, scientific competitions like ImageNet Large Scale Visual Recognition Challenges are providing evidence that computers can achieve human-like competence in image recognition. There are numerous computer models in medical diagnosis to help physicians. Among different models, deep learning algorithms, in particular convolutional neural networks are among the first choices for medical images analysis. This paper use one of the largest dataset of open-source musculoskeletal radiographs (MURA) for abnormality detection of thousands of musculoskeletal radiographs based on the deep learning to build models for detecting and localizing the abnormalities.
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N. Milosevic, A. Dehghantanha, K.K.R. Choo, Machine learning aided Android malware classification. Comput. Electr. Eng. 61, 266–274 (2017)
O.M.K. Alhawi, J. Baldwin, A. Dehghantanha, Leveraging machine learning techniques for windows ransomware network traffic detection, in Advances in Information Security, (Springer, Cham, 2018)
A. Shalaginov, S. Banin, A. Dehghantanha, K. Franke, Machine learning aided static malware analysis: a survey and tutorial, in Advances in Information Security, (Springer, Cham, 2018)
A. Eliasy, R. Ambrosio, B.T. Lopes, Artificial intelligence in corneal diagnosis: where are we? in Current Ophthalmology Reports, (Springer, Cham, 2019), pp. 1–8
A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau, S. Thrun, Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115 (2017)
O. Osanaiye, H. Cai, K.K.R. Choo, A. Dehghantanha, Z. Xu, M. Dlodlo, Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. EURASIP J. Wirel. Commun. Netw. 2016, 130 (2016)
H. Karimipour, V. Dinavahi, On false data injection attack against dynamic state estimation on smart power grids, in 2017 5th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2017 (2017)
J. Sakhnini, H. Karimipour, A. Dehghantanha, Smart grid cyber attacks detection using supervised learning and heuristic feature selection, in IEEE Int. Conf. on Smart Energy Grid Engineering, Oshawa (2019)
H.K.S. Geris, Feature selection-based approach for joint cyber-attack detection and state estimation, in IEEE Int. Conf. on Smart Energy Grid Engineering, Oshawa (2019)
S. Grooby, T. Dargahi, A. Dehghantanha, A bibliometric analysis of authentication and access control in IoT devices, in Handbook of Big Data and IoT Security, (Springer, Cham, 2019)
M. Keil, E.H. Park, B. Ramesh, Violations of health information privacy: the role of attributions and anticipated regret in shaping whistle-blowing intentions. Inf. Syst. J. 28, 818–848 (2018)
C.S. Kruse, B. Smith, H. Vanderlinden, A. Nealand, Security techniques for the electronic health records. J. Med. Syst. 41, 127 (2017)
S. Walker-Roberts, M. Hammoudeh, A. Dehghantanha, A systematic review of the availability and efficacy of countermeasures to internal threats in healthcare critical infrastructure. IEEE Access 6, 25167–25177 (2018)
W. Meng, K.R. Choo, S. Furnell, A.V. Vasilakos, C.W. Probst, Towards Bayesian-based Trust Management for Insider Attacks in healthcare software-defined networks, in IEEE Transactions on Network and Service Management 15(2), 761–773 (June 2018)
P.C. Evans, M. Annunziata, Industrial Internet: Pushing the Boundaries of Minds and Machines (General Electric, Tech. Rep, 2012)
M. Begli, F. Derakhshan, H. Karimipour, A layered intrusion detection system for critical infrastructure using machine learning, in IEEE Int. Conf. on Smart Energy Grid Engineering, Oshawa (2019)
Y. Wang, L.A. Kung, T.A. Byrd, Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Chang. 126, 3–13 (2018)
F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, Y. Wang, Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol 2, 230–243 (2017)
M. Coeckelbergh, Health care, capabilities, and AI assistive technologies. Ethical Theory Moral Pract. 13, 181–190 (2010)
W.N. Price, I.G. Cohen, Privacy in the age of medical big data. Nat. Med. 25, 37–43 (2019)
H. HaddadPajouh, A. Dehghantanha, R. Khayami, K.K.R. Choo, A deep Recurrent Neural Network based approach for internet of Things malware threat hunting. Futur. Gener. Comput. Syst. 85, 88–96 (2018)
Z. Jiao, X. Gao, Y. Wang, J. Li, A deep feature based framework for breast masses classification. Neurocomputing 197, 221–231 (2016)
Q. Dou, H. Chen, Y. Jin, L. Yu, J. Qin, P.A. Heng, 3D deeply supervised network for automatic liver segmentation from CT volumes, in Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2016)
H. Karimipour, A. Dehghantanha, R.M. Parizi, K.R. Choo, H. Leung, A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids. IEEE Access 7, 80778–80788 (2019)
K. Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193–202 (1980)
Y. Le Cun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, vol. 1 551, 541 (1989)
H. Karimipour, V. Dinavahi, Robust massively parallel dynamic state estimation of power systems against cyber-attack. IEEE Access 6, 2984–2995 (2017)
H. Karimipour, V. Dinavahi, Extended Kalman filter-based parallel dynamic state estimation. IEEE Trans. Smart Grid 6, 1539–1549 (2015)
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems, (2012)
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2016)
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2015)
X. Han, Y. Zhong, L. Cao, L. Zhang, Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sens. 9, 848 (2017)
G. Zeng, Y. He, Z. Yu, X. Yang, R. Yang and L. Zhang, "Going Deeper with Convolutions," arXiv 1409, 4842 (2014)
B.Q. Huynh, H. Li, M.L. Giger, Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J. Med. Imag. 3, 034501 (2016)
J. Blumberg, G. Kreiman, How cortical neurons help us see: visual recognition in the human brain. J. Clin. Invest. 120, 3054–3063 (2010)
S. Mohammadi, H. Mirvaziri, M. Ghazizadeh-Ahsaee, H. Karimipour, Cyber intrusion detection by combined feature selection algorithm. J. Inform. Secur. Appl. 44, 80–88 (2019)
S. Mohammadi, V. Desai, H. Karimipour, Multivariate mutual information-based feature selection for cyber intrusion detection, in 2018 IEEE Electrical Power and Energy Conference, EPEC 2018 (2018)
T.D. Kulkarni, W.F. Whitney, P. Kohli, J.B. Tenenbaum, Deep convolutional inverse graphics network, in Advances in Neural Information Processing Systems, (2015)
D.-X. Zhou, Universality of deep convolutional neural networks, in Applied and Computational Harmonic Analysis, (2019)
S. Li, W. Song, H. Qin, A. Hao, Deep variance network: an iterative, improved CNN framework for unbalanced training datasets. Pattern Recogn. 81, 294–308 (2018)
S. Li, W. Song, H. Qin, A. Hao, Deep variance network: An iterative, improved CNN framework for unbalanced training datasets. Pattern Recogn. 81, 294–308 (2018)
S. Saha, E. Lange and S. Hehl-Lange, “A Comprehensive Guide to Convolutional Neural Networks- the ELI5 way”, 2005, available online at: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
T. Hope, Y.S. Resheff, I. Lieder, Learning TensorFlow: A Guide to Building Deep Learning Systems (O’Reilly Media Inc., 2016)
N. Shukla, Machine Learning with Tensorflow, 2018
E.M. Dovom, A. Azmoodeh, A. Dehghantanha, D.E. Newton, R.M. Parizi, H. Karimipour, Fuzzy pattern tree for edge malware detection and categorization in IoT. J. Syst. Archit. 97, 1–7 (2019)
A. Azmoodeh, A. Dehghantanha, K.-K.R. Choo, Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning. IEEE Trans. Sustain. Comput. 4, 88–95 (2018)
F. Nelli, F. Nelli, Deep learning with TensorFlow, in Python Data Analytics, (2018)
L. Carin, M.J. Pencina, On Deep Learning for Medical Image Analysis (2018)
J. Archenaa, E.A.M. Anita, A survey of big data analytics in healthcare and government. Proc. Comput. Sci. 50, 408–413 (2015)
K. Suzuki et al., Radiol. Phys. Technol. 10, 257–273 (2017)
A. Azmoodeh, A. Dehghantanha, M. Conti, K.K.R. Choo, Detecting crypto-ransomware in IoT networks based on energy consumption footprint. J. Ambient. Intell. Humaniz. Comput. 9, 1141–1152 (2018)
G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J.A.W.M. Laak, B. Ginneken, C.I. Sánchez, A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J.A.W.M. Laak, B. Ginneken, C.I. Sánchez, A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
G.E. Hinton, “A practical guide to training restricted Boltzmann machines machine learning group”, Univ. of Toronto, Toronto, ON, Canada, tech. Rep., 2010–2003 (2010)
J. Arevalo, F.A. González, R. Ramos-Pollán, J.L. Oliveira, M.A. Guevara Lopez, Representation learning for mammography mass lesion classification with convolutional neural networks. Comput. Methods Prog. Biomed. 127, 248–257 (2016)
P. Moeskops, M.A. Viergever, A.M. Mendrik, L.S. De Vries, M.J.N.L. Benders, I. Isgum, Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1252–1261 (2016)
J. Kawahara, G. Hamarneh, Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers, in Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2016)
N. Tajbakhsh, K. Suzuki, Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs. Pattern Recogn. 63, 476–486 (2017)
P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K. Shpanskaya, M.P. Lungren, CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv.org (2017)
G. Huang, Z. Liu, L. van der Maaten, K. Q. Weinberger, Densely connected convolutional networks, in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (2017)
P. Rajpurkar, J. Irvin, A. Bagul, D. Ding, T. Duan, H. Mehta, B. Yang, K. Zhu, D. Laird, R.L. Ball, C. Langlotz, MURA: large dataset for abnormality detection in musculoskeletal radiographs. arXiv (2018)
Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, H. Greenspan, Chest pathology detection using deep learning with non-medical training, in Proceedings - International Symposium on Biomedical Imaging (2015)
P. Beyer, S. Mihowicz, M. Boistelle, 2018-diagnose like a radiologist: attention guided convolutional neural network for thorax disease classification. Sem. des Hopitaux (1975)
D.P. MacKinnon, Introduction to Statistical Mediation Analysis (Taylor and Francis Group, London, 2014)
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Han, W., Azmoodeh, A., Karimipour, H., Yang, S. (2020). Privacy Preserving Abnormality Detection: A Deep Learning Approach. In: Choo, KK., Dehghantanha, A. (eds) Handbook of Big Data Privacy. Springer, Cham. https://doi.org/10.1007/978-3-030-38557-6_13
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