Abstract
Chest X-ray scan is a most often used modality by radiologists to diagnose many chest related diseases in their initial stages. The proposed system aids the radiologists in making decision about the diseases found in the scans more efficiently. Our system combines the techniques of image processing for feature enhancement and deep learning for classification among diseases. We have used the ChestX-ray14 database in order to train our deep learning model on the 14 different labeled diseases found in it. The proposed research shows the significant improvement in the results by using wavelet transforms as pre-processing technique.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wang, X., et al.: Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Singh, G., Verma, D., Koranga, P.: Tech Scholar: Application of Wavelet Transform on Images: A Review (2016)
Li, L., et al.: Wavelet transform for directional feature extraction in medical imaging. In: Proceedings of International Conference on Image Processing, vol. 3. IEEE (1997)
Wang, A., Sun, H., Guan, Y.: The application of wavelet transform to multi-modality medical image fusion. In: 2006 IEEE International Conference on Networking, Sensing and Control. IEEE (2006)
Sifuzzaman, M., Islam, M.R., Ali, M.Z.: Application of wavelet transform and its advantages compared to Fourier transform (2009)
Chaplot, S., Patnaik, L.M., Jagannathan, N.R.: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed. Sign. Process. Control 1(1), 86–92 (2006)
Mirzaalian, H., et al.: Various applying of wavelet transform in digital mammograms for detecting masses and microcalsifications. In: MVA (2007)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition (CVPR) (2015)
Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 (2017)
Roth, H.R., et al.: Anatomy-specific classification of medical images using deep convolutional nets. arXiv preprint arXiv:1504.04003 (2015)
Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
Lakhani, P., Sundaram, B.: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2), 574–582 (2017)
Yu, Y., et al.: Deep transfer learning for modality classification of medical images. Information 8(3), 91 (2017)
Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018)
Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2009)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, CoRR, vol. abs/1512.03385 (2015)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)
Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4-5), 198–211 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rasheed, A., Younis, M.S., Bilal, M., Rasheed, M. (2020). Classification of Chest Diseases Using Wavelet Transforms and Transfer Learning. In: Su, R., Liu, H. (eds) Medical Imaging and Computer-Aided Diagnosis. MICAD 2020. Lecture Notes in Electrical Engineering, vol 633. Springer, Singapore. https://doi.org/10.1007/978-981-15-5199-4_16
Download citation
DOI: https://doi.org/10.1007/978-981-15-5199-4_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5198-7
Online ISBN: 978-981-15-5199-4
eBook Packages: EngineeringEngineering (R0)