Abstract
This paper proposes a Deep Convolutional Neural Network (DCNN) architecture for biomedical image segmentation. The proposed work explores the role of separable convolution within the context of biomedical image segmentation. The proposed architecture, XceptionUnetV1, is an amalgam of the Xception and U-Net models. Additionally, the model uses dilated convolution to increase the field view of the filters. The proposed model achieves better results than various versions of U-Net. The proposed DCNN architecture requires much lesser parameters, approximately \(1/4^{th}\) of the U-Net model. The proposed model has been tested on chest X-rays for lung segmentation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ronneberger, O., Fischer, P., Brox, T.: Convolutional Networks for Biomedical Image Segmentation, U-Net (2015)
Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual U-Net. IEEE Geosci. Remote Sens. Lett. 15(5), 749–753 (2018)
Wang, S., et al.: U-Net using stacked dilated convolutions for medical image segmentation (2020)
Wang, J., Lv, P., Wang, H., Shi, C.: SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography (2021)
Chollet, F. Xception: deep learning with depthwise separable convolutions (2017)
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: Semantic image segmentation with deep convolutional nets and fully connected CRFs (2016)
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs (2017)
Jaeger, S., Candemir, S., Antani, S., Wáng, Y.X.J., Lu, P.X., Thoma, G.: Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg. 4(6), 475 (2014)
Higgins, G., et al.: Final report of the meeting “modeling & simulation in medicine: towards an integrated framework”: July 20-21, 2000, national library of medicine, National Institutes of Health, Bethesda, Maryland, USA. Comput. Aided Surg. 6(1), 32–39 (2001)
Jaeger, S., et al.: Automatic tuberculosis screening using chest radiographs. IEEE Trans. Med. Imaging 33, 233–245 (2014)
Candemir, S., et al.: Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans. Med. Imaging 33, 577–590 (2014)
Kingma, D., Ba, J.: A Method for Stochastic Optimization. Adam (2017)
Chollet, F., et al.: Keras (2015). https://keras.io
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Faiz, M.F.I., Iqbal, M.Z. (2022). XceptionUnetV1: A Lightweight DCNN for Biomedical Image Segmentation. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-99619-2_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99618-5
Online ISBN: 978-3-030-99619-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)