Abstract
Breast cancer is the most deadly cancer in females globally. Architectural distortion is the third most often reported irregularity on digital mammograms among the masses and microcalcification. Physically identifying architectural distortion for radiologists is problematic because of its subtle appearance on the dense breast. Automatic early identification of breast cancer using computer algorithms from a mammogram may assist doctors in eliminating unwanted biopsies. This research presents a novel diagnostic method to identify AD ROIs from mammograms using computer vision-based depth-wise CNN. The proposed methodology is examined on private PINUM 2885 and public DDSM 3568 images and achieved a 0.99 and 0.95 sensitivity, respectively. The experimental findings revealed that the proposed scheme outperformed SVM, KNN, and previous studies.
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
WHO: Fact Sheet World Health Organization (2018). https://www.who.int/news-room/fact-sheets/detail/cancer
Paramkusham, S., Rao, K., Rao, B.P.: Automatic detection of breast lesion contour and analysis using fractals through spectral methods. In: Proceedings of the International Conference on Advances in Computer Science, AETACS, National Capital Region, India, pp. 13–14 (2013)
Bahl, M., Lamb, L.R., Lehman, C.D.: Pathologic outcomes of architectural distortion on digital 2d versus tomosynthesis mammography. Am. J. Roentgenol. 209(5), 1162–1167 (2017)
American College of Radiology: Mammography and Breast Imaging Resoruces (2019). https://www.acr.org/Clinical-Resources/Breast-Imaging-Resources
Nemoto, M., Honmura, S., Shimizu, A., Furukawa, D., Kobatake, H., Nawano, S.: A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows. Int. J. Comput. Assist. Radiol. Surg. 4(1), 27–36 (2009)
Minavathi, Murali, S., Dinesh, M., et al.: Model based approach for detection of architectural distortions and spiculated masses in mammograms. Int. J. Comput. Sci. Eng. 3(11), 3534 (2011). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.302.6348 &rep=rep1 &type=pdf
Banik, S., Rangayyan, R.M., Desautels, J.L.: Detection of architectural distortion in prior mammograms. IEEE Trans. Med. Imaging 30(2), 279–294 (2010)
Jasionowska, M., Przelaskowski, A., Rutczynska, A., Wroblewska, A.: A two-step method for detection of architectural distortions in mammograms. In: Information Technologies in Biomedicine, pp. 73–84. Springer (2010). https://doi.org/10.1007/978-3-642-13105-9_8
Kamra, A., Jain, V., Singh, S., Mittal, S.: Characterization of architectural distortion in mammograms based on texture analysis using support vector machine classifier with clinical evaluation. J. Digit. Imaging 29(1), 104–114 (2016)
Liu, X., Zhai, L., Zhu, T., Yang, Z.: Architectural distortion recognition based on a subclass technique and the sparse representation classifier. In: 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 422–426. IEEE (2016)
Casti, P., et al.: Contour-independent detection and classification of mammographic lesions. Biomed. Signal Process. Control 25, 165–177 (2016)
de Oliveira, H.C., et al.: A cross-cutting approach for tracking architectural distortion locii on digital breast tomosynthesis slices. Biomed. Signal Process. Control 50, 92–102 (2019)
Guan, Y., et al.: Detecting asymmetric patterns and localizing cancers on mammograms. Patterns 1(7), 100106 (2020)
Cai, Q., Liu, X., Guo, Z.: Identifying architectural distortion in mammogram images via a se-densenet model and twice transfer learning. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–6. IEEE (2018)
Wu, N., et al.: Deep neural networks improve radiologists? Performance in breast cancer screening. IEEE Trans. Med. Imaging 39(4), 1184–1194 (2019)
Khan, H.N., Shahid, A.R., Raza, B., Dar, A.H., Alquhayz, H.: Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access 7, 165724–165733 (2019)
Pakistan Atomic Energy Commission: Punjab Institute of Nuclear Medicine (PINUM Faisalabad) (2020). http://www.paec.gov.pk/Medical/Centres/
The Cancer Imaging Archive: Digital Database for Screening Mammography (DDSM) (2021). https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM
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 Singapore Pte Ltd.
About this paper
Cite this paper
ur Rehman, K., Li, J., Pei, Y., Yasin, A., Ali, S. (2022). A Deep Learning-Based Approach for Mammographic Architectural Distortion Classification. In: Pei, Y., Chang, JW., Hung, J.C. (eds) Innovative Computing. IC 2022. Lecture Notes in Electrical Engineering, vol 935. Springer, Singapore. https://doi.org/10.1007/978-981-19-4132-0_1
Download citation
DOI: https://doi.org/10.1007/978-981-19-4132-0_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-4131-3
Online ISBN: 978-981-19-4132-0
eBook Packages: Computer ScienceComputer Science (R0)