Abstract
Convolutional Neural Network (CNN) has made remarkable progress in the medical field. The use of CNN is widely necessary to extract highly representative characteristics in the case of acute medical pathology. Composed of fully connected layers, the CNN allows the classification of the data. The classification process is done among the network layers by filtering, selecting, and applying these features at the last layers. CNN offers a better prognosis, especially in the case of colorectal cancer (CRC) prevention. CRC develops from cells that line the inner lining of the colon. Mostly, it comes from a benign tumor, called a polyp, which slowly grows with time to develop into malignant cells. However, classification of 3D scan images of the abdomen based on the presence or absence of polyps is necessary to increase the chance of early detection of the disease and thus guide it to the appropriate treatment. In this work, we present and study a 3D CNN model for the processing and classification of polyps. The results show promising performances for a 12 layers 3D CNN model.
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
References
Ferlay, J., Soerjomataram, I., Dikshit, R., et al.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012: Globocan 2012. Int. J. Cancer 136(5), 359–386 (2015). https://doi.org/10.1002/ijc.29210
Zhang, R., Zheng, Y., Poon, C., et al.: Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker. Pattern Recognit. 83, 209–219 (2018). https://doi.org/10.1016/j.patcog.2018.05.026
Ribeiro, E., Uhl, A., Wimmer, G., et al.: Exploring deep learning and transfer learning for colonic polyp classification. Comput. Math. Methods Med. 2016, 1–16 (2016). https://doi.org/10.1155/2016/6584725
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A.W.M., van Ginneken, B., Sánchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017). https://doi.org/10.1016/j.media.2017.07.005
Chang, A.X., Funkhouser, T., Guibas, L., et al.: ShapeNet: an information-rich 3D model repository. arXiv:1512.03012 [cs] (2015)
Yang, D., Zhang, S., Yan, Z., et al.: Automated anatomical landmark detection on distal femur surface using convolutional neural network. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 17–21. IEEE, Brooklyn, NY, USA (2015). https://doi.org/10.1109/ISBI.2015.7163806
Singh, S.P., Wang, L., Gupta, S., et al.: 3D deep learning on medical images: a review. Sensors 20(18), 5097 (2020). https://doi.org/10.3390/s20185097
Zhou, J., Luo, L.-Y., Dou, Q., et al.: Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images. J. Magn. Reson. Imaging 50(4), 1144–1151 (2019). https://doi.org/10.1002/jmri.26721
Kawahara, J., Brown, C.J., Miller, S.P., et al.: BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146, 1038–1049 (2017)
Nie, D., Zhang, H., Adeli, E., et al.: 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. In: Medical Image Computing and Computer-Assisted Intervention: MICCAI. International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 9901, pp. 212–220 (2016)
Payan, A., Montana, G.: Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. arXiv:1502.02506 (2015)
Smith, K., Clark, K., Bennett, W., et al.: Data from CT_COLONOGRAPHY. The cancer imaging archive. https://doi.org/10.7937/K9/TCIA.2015.NWTESAY1 (2015)
Johnson, C.D., Dachman, A., Cheema, J.I., et al.: Accuracy of CT colonography for detection of large adenomas and cancers. N. Engl. J. Med. 11 (2008)
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., Prior, F.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013). https://doi.org/10.1007/s10278-013-9622-7
Laghmati, S., Cherradi, B., Tmiri, A., et al.: Classification of patients with breast cancer using neighbourhood component analysis and supervised machine learning techniques. In: 2020 3rd International Conference on Advanced Communication Technologies and Networking (CommNet), pp. 1–6. IEEE, Marrakech, Morocco (2020). https://doi.org/10.1109/CommNet49926.2020.9199633
Laghmati, S., Tmiri, A., Cherradi, B.: Machine learning based system for prediction of breast cancer severity. In: 2019 International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 1–5. IEEE, Fez, Morocco (2019). https://doi.org/10.1109/WINCOM47513.2019.8942575
Acknowledgment
The authors would like to express their special thanks of gratitude to the late Professor Nabil ELMARZOUQI, who contributed considerably to this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hicham, K., Laghmati, S., Tmiri, A. (2023). Artificial Intelligence for Colorectal Polyps Classification Using 3D CNN. In: Azrar, L., et al. Advances in Integrated Design and Production II. CIP 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-23615-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-23615-0_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23614-3
Online ISBN: 978-3-031-23615-0
eBook Packages: EngineeringEngineering (R0)