Abstract
Pneumoconiosis is one of the most universal and severe occupational diseases in the world today. The diagnosis of pneumoconiosis mainly depends on the doctors’ analysis of chest X-ray films. However, diagnostic accuracy is related to a doctor’s expertise. Recent research has suggested that deep convolutional neural network (CNN) could better identify diseases than experts. Deep CNN can efficiently extract features from data for discrimination. Nevertheless, high volumes of training data are usually necessary to achieve, which is not desirable from the research. This article studies pneumoconiosis identification from the perspective of CNN-based transfer learning. In this work, we raise two transfer learning patterns from “frozen layers” and “finetuned layers” to solve those problems. The knowledge learned from the ImageNet is transferred to pneumoconiosis identification. Also, we propose some methods of image denoising, lung segmentation, and data amplification for preprocessing to improve image qualities. The experimental results show that both transfer learning patterns are better than starting from scratch with limited training data. Notably, the “finetuned layers” transfer learning pattern achieves a higher performance.
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Annarumma, M., Withey, S.J., Bakewell, R.J., Pesce, E., Goh, V., Montana, G.: Automated triaging of adult chest radiographs with deep artificial neural networks. Radiology 1(1), 196–202 (2019)
Castranova, V., Vallyathan, V.: Silicosis and coal workers’ pneumoconiosis. Environ. Health Perspect. 108(4), 675–684 (2000)
Chen, B., Li, H., Huang, J.: Image processing operations identification via convolutional neural network. Sci. China Inform Sci. 63(3): 139109:1–3 (2020)
Chong, S., Lee, K.S., Chung, M.J., Han, J., Kwon, O.J., Kim, T.S.: Pneumoconiosis: comparison of imaging and pathologic findings. Radiographics 26(1), 59–77 (2006)
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: ImageNet: a large-scale hierarchical image database. In: Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Devnath, L., Luo, S., Summons, P., Wang, D.: An accurate black lung detection using transfer learning based on deep neural networks. In: Proceedings of 2019 International Conference on Image and Vision Computing New Zealand, pp. 1–6. IEEE (2019)
Gao, M., Bagci, U., Lu, L., Wu, A., Buty, M., Shin, H., Roth, H., Papadakis, G.Z., Depeursinge, A., Summers, R.M., Xu, Z., Mollura, D.J.: Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput. Methods Biomech. Biomed. Eng. Imaging Vis 6(1), 1–6 (2018)
Gao, F., Zhu, Y., Zhang, J.: Artifical intelligence in computer-aided diagnosis of abdomen diseases. Sci. China Life Sci. 62(10), 1396–1399 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jiang, L., Xie, H., Pan, B.: Speeding up digital image correlation computation using the integral image technique. Opt. Lasers Eng. 65, 117–122 (2015)
Karimollah, H.T.: Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J. Internal Med. 4(2), 627–635 (2013)
Kermany, D.S., Goldbaum, M., Cai, W., Valentim, C.C., Liang, H., Baxter, S.L., McKeown, A., Yang, G., Wu, X., Yan, F., Dong, J., Prasadha, M.K., Pei, J., Ting, M.Y., Zhu, J., Li, C., Hewett, S., Dong, J., Ziyar, I., Shi, A., Zhang, R., Zheng, L., Hou, R., Shi, W., Fu, X., Duan, Y., Huu, V.A., Wen, C., Zhang, E.D., Zhang, C.L., Li, O., Wang, X., Singer, M.A., Sun, X., Xu, J., Tafreshi, A., Lewis, M.A., Xia, H., Zhang, K.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018)
Konečnỳ, J., Liu, J., Richtárik, P., Takáč, M.: Mini-batch semi-stochastic gradient descent in the proximal setting. IEEE J. Select Top. Signal Process. 10(2), 242–255 (2015)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Okumura, E., Kawashita, I., Ishida, T.: Computerized classification of pneumoconiosis on digital chest radiography artificial neural network with three stages. J. Digit. Imaging 30, 413–426 (2017)
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: PyTorch: an imperative style, high-performance deep learning library. In: Proccedings of the 33rd Conference on Neural Information System, pp. 1–12 (2019)
Salem, M., Taheri, S., Yuan, J.: ECG arrhythmia classification using transfer learning from 2-dimensional deep CNN features. In: Proceedings of 2018 IEEE Conference on Biomedical Circuits and Systems, pp. 1–4. IEEE (2018)
Shao, W., Ding, Y., Shen, H., Zhang, D.: Deep model-based feature extraction for predicting protein subcellular localizations from bio-images. Front. Comput. Sci. 11, 243–252 (2017)
Shie, C., Chuang, C., Chou, C., Wu, M., Chang, E. Y.: Transfer representation learning for medical image analysis. In: Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 711–714. IEEE (2015)
Shin, H., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
Wang, S., Shi, J., Ye, Z., Dong, D., Yu, D., Zhou, M., Liu, Y., Gevaert, O., Wang, K., Zhu, Y., Zhou, H., Liu, Z., Tian, J.: Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. Eur Respir J 53(3), 1800986:1–13 (2019)
Zhang, X., Wang, L., Xie, J., Zhu, P.: Human-in-the-loop image segmentation and annotation. Sci. China Inform. Sci. 63(11) 219101:1–3 (2020)
Zhou, Z., Shin, J., Zhang, L., Gurudu, S., Gotway, M., Liang, J.: Fine-tuning convolutional neural networks for biomedical image analysis: actively and incrementally. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7340–7351 (2017)
Zhu, B., Luo, W., Li, B., Chen, B., Yang, Q., Xu, Y., Wu, X., Chen, H., Zhang, K.: The development and evaluation of a computerized diagnosis scheme for pneumoconiosis on digital chest radiographs. BioMed. Eng. Online 13, 141 (2014)
Acknowledgements
We would like to thank the anonymous reviewers for their insightful comments and valuable feedback. This work is supported by the medical and health big data center project from Hubei Provincial Development and Reform Commission.
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Zheng, R., Zhang, L. & Jin, H. Pneumoconiosis identification in chest X-ray films with CNN-based transfer learning. CCF Trans. HPC 3, 186–200 (2021). https://doi.org/10.1007/s42514-021-00067-8
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DOI: https://doi.org/10.1007/s42514-021-00067-8