Abstract
The deep convolutional neural networks are most trustable and reliable approach to solve any kind of problems in these days. Especially, in recent days’ mammography image analysis uses deep neural network to identify the early stage cancer and it became most acceptable over other machine learning algorithms. These approaches are supporting radiologists to detect suspicious mass variability and other key characteristics in mammography images with good accuracy through greater performance. However, the performance and accuracy of these computer-aided breast cancer detection systems depends on factors, such as quality mammography images, selections of DNN architecture with appropriate hyper parameters. In this work, proposed a new customized convolutional neural network architecture to identify cancerous and healthy breast using mammography images obtained from digital database of screening mammography. The proposed model performed efficiently when compared with other previously proposed models in detection. Cancerous and healthy measured in terms of accuracy and area under curve (AUC) with optimized parameters and cost.
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The data set generated and analyzed during the current study are available from the corresponding author on reasonable request.
References
Sung H, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660.
Giaquinto AN, et al. Breast cancer statistics, 2022. CA Cancer J Clin. 2022;72(6):524–41. https://doi.org/10.3322/caac.21754.
Harbeck N, et al. Breast cancer. Nat Rev Dis Prim. 2019;5(1):66. https://doi.org/10.1038/s41572-019-0111-2.
Lauby-Secretan B, et al. Breast-cancer screening–viewpoint of the IARC Working Group. N Engl J Med. 2015;372(24):2353–8. https://doi.org/10.1056/NEJMsr1504363.
Zhao Z-Q, Zheng P, Xu S-T, Wu X. Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst. 2019. https://doi.org/10.1109/TNNLS.2018.2876865.
Cantone M, Marrocco C, Tortorella F, Bria A. Convolutional networks and transformers for mammography classification: an experimental study. Sensors. 2023. https://doi.org/10.3390/s23031229.
Thapa A, et al. Deep learning for breast cancer classification: enhanced tangent function. Comput Intell. 2022;38(2):506–29. https://doi.org/10.1111/coin.12476.
Zahoor S, Shoaib U, Lali IU. Breast cancer mammograms classification using deep neural network and entropy-controlled whale optimization algorithm. Diagnostics (Basel, Switzerland). 2022. https://doi.org/10.3390/diagnostics12020557.
Malebary S, Hashmi A. Automated breast mass classification system using deep learning and ensemble learning in digital mammogram. IEEE Access. 2021. https://doi.org/10.1109/ACCESS.2021.3071297.
Tyagi A, et al. Nicotine promotes breast cancer metastasis by stimulating N2 neutrophils and generating pre-metastatic niche in lung. Nat Commun. 2021;12(1):474. https://doi.org/10.1038/s41467-020-20733-9.
Salama W, Elbagoury A, Aly M. Novel breast cancer classification framework based on deep learning. IET Image Process. 2020. https://doi.org/10.1049/iet-ipr.2020.0122.
Mohapatra S, Muduly S, Mohanty S, Ravindra JVR, Mohanty SN. Evaluation of deep learning models for detecting breast cancer using histopathological mammograms Images. Sustain Oper Comput. 2022;3:296–302.
Ahsan MM et al. Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights. arXiv Prepr. arXiv2211.05633, 2022.
Ming Y, et al. Progress and future trends in PET/CT and PET/MRI molecular imaging approaches for breast cancer. Front Oncol. 2020;10:1301.
Saxena S, Gyanchandani M. A model for classification of wisconsin breast cancer datasets using principal component analysis and backpropagation neural network. Int J Sci Res. 2019;8(7):1324–27.
Chikarmane SA, Cochon LR, Khorasani R, Sahu S, Giess CS. Screening mammography performance metrics of 2D Digital mammography versus digital breast tomosynthesis in women with a personal history of breast cancer. Am J Roentgenol. 2021;217(3):587–94. https://doi.org/10.2214/AJR.20.23976.
Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP. The digital database for screening mammography. In: Proceedings of the Fifth International Workshop on Digital Mammography, M.J. Yaffe, ed., 212-218, Medical Physics Publishing; 2001. ISBN 1-930524-00-5.
Clark K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26(6):1045–57. https://doi.org/10.1007/s10278-013-9622-7.
Jmour N, Zayen S, Abdelkrim A. Convolutional neural networks for image classification. In: 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), 2018; pp. 397–402, https://doi.org/10.1109/ASET.2018.8379889.
Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2010;22(10):1345–59. https://doi.org/10.1109/TKDE.2009.191.
Juneja M, Vedaldi A, Jawahar CV, Zisserman A. Blocks that shout: distinctive parts for scene classification. In: 2013 IEEE Conference on computer vision and pattern recognition, 2013; pp. 923–930, https://doi.org/10.1109/CVPR.2013.124.
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The Vijayanagara Sri Krishnadevaraya University, Ballari provided the facilities needed to conduct the research, which the authors gratefully acknowledged.
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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.
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Kadadevarmath, J., Reddy, A.P. Customized Convolutional Neural Network for Breast Cancer Classification. SN COMPUT. SCI. 5, 207 (2024). https://doi.org/10.1007/s42979-023-02469-7
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DOI: https://doi.org/10.1007/s42979-023-02469-7