Abstract
With the rapid development of the Internet, image information is explosively growing. Traditional image classification methods are difficult to deal with huge image data and cannot meet people’s requirements on the accuracy and speed of image classification. In recent years, the convolutional neural network (CNN) has been developing rapidly, and it has performed extremely well. The image classification method based on CNN breaks through the bottleneck of traditional image classification methods and becomes the mainstream image classification algorithm at present. CT image classification algorithm is one of the research hot spots in the field of medical image. The purpose of this paper is to apply convolutional neural network to CT image classification, so as to speed up CT image classification and improve the accuracy of CT image classification and so as to reduce the workload of doctors and improve work efficiency. In this paper, CT images are classified by CDBN model. Vector machine SVM is used as the feature classifier of CDBN model to enhance feature transfer and reuse so as to enrich the features. It also suppresses features that are not very useful for current tasks and improves the performance of the model. Using CDBN to classify CT images, several commonly used gray images are compared. Comparing the results of the ordinary gradient algorithm with Adam algorithm, we can get the CDBN model using Adam optimization algorithm. In CT image classification, both accuracy and speed have a good effect. The experimental results show that the training speed of CDBN model of Adam optimization algorithm in CT image classification is 3% faster than that of general gradient algorithm.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Filho PPR, Rebouças EDS, Marinho LB et al (2017) Analysis of human tissue densities: a new approach to extract features from medical images. Pattern Recogn Lett 94:211–218
Dandıl E (2018) A computer-aided pipeline for automatic lung cancer classification on computed tomography scans. J Healthc Eng 2018(4):1–12
Sethi G, Rawal K, Saini BS (2018) Computer aided diagnosis from CT images using wavelet features with progressive classification. In: International conference on intelligent circuits and systems (ICICS). pp 58–62
Filho PPR, Barros ACdS, Ramalho GLB et al (2019) Automated recognition of lung diseases in CT images based on the optimum-path forest classifier. Neural Comput Appl 31:901–914
Sebatubun MM, Haryawan C, Windarta B (2017) Classification of ground glass opacity lesion characteristic based on texture feature using lung CT image. J Exp Theor Artif Intell 30(2):1–13
Fang Y, Chi Z, Yang W et al (2018) Blind visual quality assessment for image super-resolution by convolutional neural network. Multimed Tools Appl 77(10):1–18
Yoon Y, Jeon HG, Yoo D et al (2017) Light-field image super-resolution using convolutional neural network. IEEE Signal Process Lett 24(6):848–852
Ševo I, Avramović A (2017) Convolutional neural network based automatic object detection on aerial images. IEEE Geosci Remote Sens Lett 13(5):740–744
Salehi SSM, Erdogmus D, Gholipour A (2017) Auto-context convolutional neural network (auto-net) for brain extraction in magnetic resonance imaging. IEEE Trans Med Imaging 36:2319–2330
Yin X, Liu X (2017) Multi-task convolutional neural network for pose-invariant face recognition. IEEE Trans Image Process 27:964–975
Valverde S, Cabezas M, Roura E et al (2017) Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. Neuroimage 155:159–168
Acharya UR, Oh SL, Hagiwara Y et al (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389–396
Rocco I, Arandjelovic R, Sivic J (2017) Convolutional neural network architecture for geometric matching. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol 99. p 1
Ma X, Dai Z, He Z et al (2017) Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4):818
Wang Z, Li J, Enoh M (2019) Removing ring artifacts in CBCT images via generative adversarial networks with unidirectional relative total variation loss. Neural Comput Appl 31:5147–5158
Chen H, Zhang Y, Kalra MK et al (2017) Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med Imaging 36:2524–2535
Moeskops P, Viergever MA, Mendrik AM et al (2016) Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35:1252–1261
Kruthiventi SSS, Ayush K, Babu RV (2017) DeepFix: a fully convolutional neural network for predicting human eye fixations. IEEE Trans Image Process 26(9):4446–4456
Anthimopoulos M, Christodoulidis S, Ebner L et al (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35:1207–1216
Acknowledgements
This study was supported by Grant No. 2018GSF118221 from the Key Research and Development Program of Shandong Province.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There are no potential competing interests in our paper. And all authors have seen the manuscript and approved to submit to your journal. We confirm that the content of the manuscript has not been published or submitted for publication elsewhere.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhang, Y., Wang, S., Zhao, H. et al. CT image classification based on convolutional neural network. Neural Comput & Applic 33, 8191–8200 (2021). https://doi.org/10.1007/s00521-020-04933-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-020-04933-4