research-article

Classification of Lung Tissue with Cystic Fibrosis Lung Disease via Deep Convolutional Neural Networks

Authors:

Xi Jiang,

Hualei ShenAuthors Info & Claims

ISICDM 2018: Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine

Pages 113 - 116

https://doi.org/10.1145/3285996.3286020

Published: 13 October 2018 Publication History

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Abstract

Quantitative classification of disease regions contained in lung tissues obtained from Computed Tomography (CT) scans is one of the key steps to evaluate lesion degrees of Cystic Fibrosis Lung Disease (CFLD). In this paper, we propose a deep Convolutional Neural Network-based (CNN) framework for automatic classification of lung tissues with CFLD. Core of the framework is the integration of deep CNNs into the classification workflow. To train and validate performance of deep CNNs, we build datasets for inspiration CT scans and expiration CT scans, respectively. We employ transfer learning techniques to fine tune parameters of deep CNNs. Specifically, we train Resnet-18 and Resnet-34 and validate the performance on the built datasets. Experimental results in terms of average precision and receiver operating characteristic curve demonstrate effectiveness of deep CNNs for classification of lung tissue with CFLD.

References

[1]

Rosenow, T., Oudraad, M. C., Murray, C. P., Turkovic, L., Kuo, W., de Bruijne, M., Ranganathan, S. C., Tiddens, H. A. and Stick, S. M. 2015. Pragma-CF. A quantitative structural lung disease computed tomography outcome in young children with cystic fibrosis. American journal of respiratory and critical care medicine, 191 (10). 1158--1165.

Google Scholar

[2]

Dy, J. G., Brodley, C. E., Kak, A., Broderick, L. S. and Aisen, A. M. 2003. Unsupervised feature selection applied to content-based retrieval of lung images. IEEE transactions on pattern analysis and machine intelligence, 25 (3). 373--378.

Digital Library

Google Scholar

[3]

Chong, D. Y., Kim, H. J., Lo, P., Young, S., McNitt-Gray, M. F., Abtin, F., Goldin, J. G. and Brown, M. S. 2016. Robustness-driven feature selection in classification of fibrotic interstitial lung disease patterns in computed tomography using 3d texture features. IEEE Transactions on Medical Imaging, 35 (1). 144--157.

Crossref

Google Scholar

[4]

Vu, T. H., Mousavi, H. S., Monga, V., Rao, G. and Rao, U. K. A. 2016. Histopathological image classification using discriminative feature-oriented dictionary learning. IEEE Transactions on Medical Imaging, 35 (3). 738--751.

Crossref

Google Scholar

[5]

Zhang, Y., Rong, J., Lu, H., Xing, Y. and Meng, J. 2017. Low-dose lung ct image restoration using adaptive prior features from full-dose training database. IEEE Transactions on Medical Imaging, 36 (12). 2510--2523.

Crossref

Google Scholar

[6]

Tulder, G. v. and Bruijne, M. d. 2016. Combining generative and discriminative representation learning for lung ct analysis with convolutional restricted boltzmann machines. IEEE Transactions on Medical Imaging, 35 (5). 1262--1272.

Crossref

Google Scholar

[7]

Christodoulidis, S., Anthimopoulos, M., Ebner, L., Christe, A. and Mougiakakou, S. 2017. Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE Journal of Biomedical and Health Informatics, 21 (1). 76--84.

Crossref

Google Scholar

[8]

Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A. and Mougiakakou, S. 2016. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Transactions on Medical Imaging, 35 (5). 1207--1216.

Crossref

Google Scholar

[9]

Li, X., Deng, Z., Deng, Q., Zhang, L., Niu, T. and Kuang, Y. 2018. A novel deep learning framework for internal gross target volume definition from 4d computed tomography of lung cancer patients. IEEE Access, 6. 37775--37783.

Crossref

Google Scholar

[10]

He, K., Zhang, X., Ren, S. and Sun, J. 2016. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition, 770--778.

Google Scholar

[11]

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A. and Bernstein, M. 2015. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115 (3). 211--252.

Digital Library

Google Scholar

Cited By

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Zhou FHu SWan XLu ZWu J(2023)Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing HomesElectronics10.3390/electronics1212258112:12(2581)Online publication date: 7-Jun-2023
https://doi.org/10.3390/electronics12122581
Tian Y(2022)Lung Function Decline Predicting Using Improved EfficientNet2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)10.1109/ICSP54964.2022.9778391(924-927)Online publication date: 15-Apr-2022
https://doi.org/10.1109/ICSP54964.2022.9778391

Index Terms

Classification of Lung Tissue with Cystic Fibrosis Lung Disease via Deep Convolutional Neural Networks
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections

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Published In

ISICDM 2018: Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine

October 2018

166 pages

ISBN:9781450365338

DOI:10.1145/3285996

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2018

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Research-article
Research
Refereed limited

Funding Sources

National Natural Science Foundation of China
Science and Technology Research Project of Henan Province
Research Start-up Foundation for Doctors in Henan Normal University
Scientific and Technological Research Project of Henan Province

Conference

ISICDM 2018

ISICDM 2018: The 2nd International Symposium on Image Computing and Digital Medicine 2018

October 13 - 14, 2018

Chengdu, China

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Cited By

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Zhou FHu SWan XLu ZWu J(2023)Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing HomesElectronics10.3390/electronics1212258112:12(2581)Online publication date: 7-Jun-2023
https://doi.org/10.3390/electronics12122581
Tian Y(2022)Lung Function Decline Predicting Using Improved EfficientNet2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)10.1109/ICSP54964.2022.9778391(924-927)Online publication date: 15-Apr-2022
https://doi.org/10.1109/ICSP54964.2022.9778391

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A Deep Learning Approach for the Segmentation of Kidney, Tumor and Cyst in Computed Tomography Scans

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