Classification of CT brain images based on deep learning networks

Computer Methods and Programs in Biomedicine

Volumn 138, Issue , 2017, Pages 49-56

  Gao, Xiaohong W ^a   Hui, Rui ^b   Tian, Zengmin ^b  

^a MIDDLESEX UNIVERSITY   (United Kingdom)

^b NAVY GENERAL HOSPITAL   (China)

Author keywords

3D CNN; Classification; Convolutional neural network; CT brain images; Deep learning; KAZE

Indexed keywords

BRAIN MAPPING; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; CONVOLUTION; DECISION MAKING; DEEP LEARNING; IMAGE CLASSIFICATION; LEARNING ALGORITHMS; NETWORK ARCHITECTURE; NEURAL NETWORKS; NEURODEGENERATIVE DISEASES;

3D CNN; BRAIN IMAGES; CLASSIFICATION ACCURACY; CLINICAL DECISION MAKING; CONVOLUTIONAL NEURAL NETWORK; CONVOLUTIONAL NEURAL NETWORKS (CNN); KAZE; SUPPLEMENTARY INFORMATION;

COMPUTERIZED TOMOGRAPHY;

ALZHEIMER DISEASE; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOANALYSIS; AVERAGING; CLASSIFICATION ALGORITHM; CLINICAL ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; CONVOLUTIONAL NEURAL NETWORK; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; EARLY DIAGNOSIS; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; NEUROIMAGING; BRAIN; DIAGNOSTIC IMAGING; LEARNING; REPRODUCIBILITY; X-RAY COMPUTED TOMOGRAPHY;

ALZHEIMER DISEASE; BRAIN; HUMANS; LEARNING; REPRODUCIBILITY OF RESULTS; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84992650028     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2016.10.007     Document Type: Article

References (26)

1. [1] BBC, The dementia timebomb. http://www.bbc.co.uk/science/0/21878238 Retrieved: 31 March 2016.
2. [2] UK Government, Dementia. https://www.gov.uk/government/news/ Retrieved: 31 March 2016.
3. [3] Zhang, Y., Londos, E., Minthon, L., Wattmo, C., Liu, H., Wahlund, L.O., Usefulness of computed tomography linear measurements in diagnosing Alzheimer's disease. Acta Radiol 49:1 (2008), 91–97.
4. [4] Oksengaard, A.R., Haakonsen, M., Dullerud, R., Engedal, K., Laake, K., Accuracy of ct scan measurements of the medial temporal lobe in routine dementia diagnostics. Int. J. Geriatr. Psychiatry 18 (2003), 308–312.
5. [5] Fukushima, K., Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cyb 36 (1980), 193–202.
6. [6] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition. IEEE 86:11 (1998), 2278–2324.
7. [7] LeCun, Y., Huang, F.J., Bottou, L., Learning methods for generic object recognition with invariance to pose and lighting. Proceedings of Computer Vision and Pattern Recognition (CVPR), 2:II97–104, 2004.
8. [8] LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature 521 (2015), 436–444.
9. [9] Ranzato, M., Huang, F.J., Boureau, Y., LeCun, Y., Unsupervised learning of invariant feature hierarchies with applications to object recognition. Proceedings of Computer Vision and Pattern Recognition (CVPR), pages 1–8, 2007.
10. [10] Ji, S., Xu, W., Yang, M., Yu, K., 3d convolutional neural networks for human action recognition. IEEE 35:1 (2015), 221–231.
11. [11] Vedaldi, A., Lenc, K., Matconvnet convolutional neural networks for matlab. Proceedings of the 23rd ACM international conference on Multimedia, pages 689–692, 2015.
12. [12] Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Li, F., Large-scale video classification with convolutional neural networks. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), pages 1725–1732, 2014.
13. [13] Gao, X., Feature-wise representation for both still and motion 3d medical images. IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pages 1–4, 2014.
14. [14] Jongen, C., Pluim, J.P.W., Nederkoorn, P.J., Viergever, M.A., Niessen, W., Construction and evaluation of an average ct brain image for inter-subject registration. Comput. Biol. Med 34:8 (2004), 647–662.
15. [15] Bishop, C.M., Pattern Recognition and Machine Learning. 2006, Springer, New York.
16. [16] Lowe, D., Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis 60 (2004), 91–110.
17. [17] Alcantarilla, P.F., Bartoli, A., Davison, A.J., Kaze features. Eur. Conf. on Computer Vision (ECCV), pages 214–227, 2012.
18. [18] Qian, Y., Hui, R., Gao, X., Retrieval of 3d surgical path based on sparse coding for image-guided neurosurgery. Signal Process 93:6 (2013), 1673–1683.
19. [19] Yang, J., Yu, K., Gong, Y., Huang, T., Linear spatial pyramid matching using sparse coding for image classification. IEEE Conference on Computer Vision and Pattern Recognition, page 1794–1801, 2009.
20. [20] Sivic, J., Zisserman, A., Kaze features. Video Google: A Text Retrieval Approach to Object Matching in Videos, pages 1470–1477, 2003.
21. [21] Qian, Y., Wang, L., Wang, C., Gao, X., The synergy of 3d sift and sparse codes for classification of viewpoints from echocardiogram videos. Greenspan, H., Muller, H., Syeda-Mahmood, T., (eds.) Medical Content-Based Retrieval for Clinical Decision Support, 2012, Springer, New York, 68–79.
22. [22] Li, W., Qian, Y., Loomes, M., Gao, X., The application of kaze feature to the classification of echocardiogram videos. MRMD 2015, LNCS, 9059:61–72, 2015.
23. [23] Catte, F., Lions, P.L., Morel, J.M., Coll, T., Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal 29 (1992), 182–193.
24. [24] Perronnin, F., Sanchez, J., Mensink, T., Improving the fisher kernel for large-scale image classification. ECCV 2010 (Part IV), 6314:143–156, 2010.
25. [25] Vedaldi, A., Fulkerson, B., Vlfeat: An open and portable library of computer vision algorithms. Proceedings of the 18th annual ACM international conference on Multimedia, 2010.
26. [26] George, A.E., de Leon, M.J., Stylopoulos, L.A., Miller, J., Kluger, A., Smith, G., et al. Ct diagnostic features of Alzheimer disease: importance of the choroidal/hippocampal fissure complex. AJNR Am. J. Neuroradiol 11:1 (1990), 101–107.