Abstract
Breast cancer is the most frequent disease among women, and it is a serious threat to their lives and well-being. Due to high population expansion, automatic mammography detection has recently become a critical concern in the medical industry. The emergence of computer-assisted systems has aided radiologists in making accurate breast cancer diagnoses. An automated detection and classification system should be implemented to prevent breast cancer from spreading. Breast densities vary widely among women, which causes missed cancers. In the case of breast density, the deep CNN algorithms can significantly reduce radiologist workload and improve risk assessment. The goal of this paper is to offer a deep learning strategy for identifying MLO and CC views of breast cancer as malignant, benign, or normal using an integration of deep convolutional neural networks (CNN) and feature fusion of LASSO (Least Absolute Shrinkage and Selection Operator) regression. The proposed method comprises pre-processing, data augmentation, feature extraction, feature fusion, and classification. The generated features were fed into LASSO regression for the best prediction in this system, which utilized CNN for feature extraction. The fused features were then transferred to CNN's fully connected layer for mammography classification. In our experiment, the publically available dataset CBIS-DDSM (Curated Breast Imaging Subset of DDSM) was employed. The proposed work gained an accuracy of 99.2%, specificity of 98.7%, AUC of 99.8%, sensitivity of 99.4%, and FI-score of 98.7%, which is higher than multi view CNN without a feature fusion based system.
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Abbreviations
- CNN:
-
Convolution neural network
- MLO:
-
Medio-lateral
- CC:
-
Cranio-caudal
- DDSM:
-
Digital database of screening mammography
- CBIS-DDSM:
-
Curated breast imaging subset of DDSM
- LASSO:
-
Least absolute shrinkage and selection operator
- CAD:
-
Computer aided diagnosis
- ROC:
-
Receiver operating characteristic
- AUC:
-
Area under ROC curve
- SVM:
-
Support vector machine
- ReLU:
-
Rectified linear unit
- ADASYN:
-
Adaptive synthetic sampling
- TP:
-
True positive
- TN:
-
True negative
- FP:
-
False positive
- FN:
-
False negative
- FC:
-
Fully connected layer
References
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2011) Global cancer statistics. Cancer J Clin 68:394–424
Cheng J-Z, Ni D, Chou Y-H, Qin J, Tiu C-M, Chang Y-C et al (2016) Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Re Ports 6(1):24454. https://doi.org/10.1038/srep24454
Dheeba J, Singh NA (2015) Computer aided intelligent breast cancer detection: second opinion for radiologists—a prospective study. Computational intelligence applications in modeling and control. Springer Cham, Switzerland, pp 397–430
Gao F, Chia K-S, Ng F-C, Ng E-H, Machin D (2002) Interval cancers following breast cancer screening in Singaporean women. Int J Cancer 101:475–479
Hua K-L, Hsu C-H, Hidayati SC, Cheng W-H, Chen Y-J (2015) Computer- aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther 8:2015–2022. https://doi.org/10.2147/OTT.S80733
Jouirou A, Baâzaoui A, Barhoumi W (2019) Multi-view information fusion in mammograms: a comprehensive overview. Inform Fusion 52:308–321. https://doi.org/10.1016/j.inffus.2019.05.001
Kallenberg M, Petersen K, Nielsen M, Ng AY, Diao P, Igel C et al (2016) Unsupervised deep learning applied to breast density segmentation and mam- mographic risk scoring. IEEE Trans Med Imaging 35(5):1322–1331
Khan HN, Shahid AR, Raza B, Dar AH, Alquhayz H (2019) Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access 7:165724–165733. https://doi.org/10.1109/ACCESS.2019.2953318
Khan HN, Shahid AR, Raza B, Dar AH, Alquhayz H (2019) Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access 7:165724–165733. https://doi.org/10.1109/ACCESS.2019.2953318
Khan TM, Shengjun Xu, Khan ZG, Uzair M, chishti. (2021) Implementing multilabeling, ADASYN, and relieff techniques for classification of breast cancer diagnostic through machine learning: efficient computer-aided diagnostic system, journal of healthcare. Engineering. https://doi.org/10.1155/2021/5577636
Kumar D, Wong A, Clausi DA (2015) Lung nodule classification using deep features in CT Images. In: Proceedings of the 12th conference on computer and robot vision (pp 133–138). https://doi.org/10.1109/CRV.2015.25
Lai ZF, Deng H (2018) Medical image classification based on deep features extracted by deep model and statistic feature fusion with multilayer perceptron. Comput Intell Neurosci. https://doi.org/10.1155/2018/2061516
Li H, Zhuang S, Li D-a, Zhao J, Ma Y (2019) Benign and malignant classification of mammogram images based on deep learning. Biomed Signal Process Control 51:347–354. https://doi.org/10.1016/j.bspc.2019.02.017
Li H, Niu J, Li D, Zhang C (2020) Classification of breast mass in two-view mammograms via deep learning. IET Image Proc. https://doi.org/10.1049/ipr2.12035
Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D (2014) Early diagnosis of Alzheimer’s disease with deep learning. In: Proceedings of the IEEE 11th international symposium on biomedical imaging (ISBI) (pp 1015–1018), https://doi.org/10.1109/ISBI.2014.6868045
Munir K, Elahi H, Ayub A, Frezza F, Rizzi A (2019) Cancer diagnosis using deep learning: a bibliographic review. Cancers 11:1235
Nahid A-A, Mehrabi MA, Kong Y (2018) Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. BioMed Res Int. https://doi.org/10.1155/2018/2362108
Nan W, Phang J, Park J, Shen Y, Huang Z, Zorin M, Jastrzebski S, Fevry T, Katsnelson J, Kim E, Wolfson S, Parikh U, Gaddam S, Lin LLY, Ho K, Weinstein JD, Reig B, Gao Y, Toth H, Pysarenko K, Lewin A, Lee J, Airola K, Mema E, Chung S, Hwang E, Naziya Samreen S, Kim G, Heacock L, Moy L, Cho K, Geras KJ (2020) Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans Med Imaging 39(4):1184–1194. https://doi.org/10.1109/TMI.2019.2945514
Online Document Breast Cancer Facts and Figures (2019) American cancer society. Atlanta, GA, USA
Saleem Z, Ramadan Fantacci, Maria E (2020) Using convolutional neural network with cheat sheet and data augmentation to detect breast cancer in mammograms. Comput Math Methods Med. https://doi.org/10.1155/2020/9523404
Shin H-C, Roth HR, Gao M et al (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imag 35(5):1285–1298
Siegel RL, Miller KD, Jemal A (2020) Cancer statistics. Cancer J Clin 70(1):7–30. https://doi.org/10.3322/caac.21590. (PMID: 31912902)
Sridevi V, Dr J, Samath A (2020) Advancement on breast cancer detection using medio-lateral-oblique (Mlo) and cranio-caudal (CC) features. Test Eng Manag 83:85–93
Sridevi V, Abdul Samath J (2019) A survey on breast cancer segmentation and classification using several methods. International journal of Scientific Research in computer science applications and management studies 8(3)
Suk H, Lee SW, Shen D (2014) Hierarchical feature representation and mul- timodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 101:69–582. https://doi.org/10.1016/j.neuroimage.2014.06.077
Suk H, Shen D (2013) Deep learning-based feature representation for AD/MCI classification. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics): 8150 (pp 583–590), LNCS https://doi.org/10.1007/978-3-642-40763-5_72
Sun L, Wang J, Zhijun H, Yong X, Cui Z (2019) Multi-view convolutional neural networks for mammographic image classification. IEEE Access 7:126273–126282. https://doi.org/10.1109/ACCESS.2019.2939167
Swiderski B, Kurek J, Osowski S, Kruk M, Barhoumi W (2016) Deep learning and non-negative matrix factorization in recognition of mammograms. In: Proceedings of the eighth international conference on graphic and image processing (ICGIP 2016), Tokyo, Japan, pp 29–31
Tang J, Member S, Rangayyan RM, Xu J, El Naqa I (2009) Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans Inf Technol Biomed 13:236–251
Wang D, Khosla A, Gargeya R, Irshad H, Beck AH (2016) Deep learning for identifying metastatic breast cancer, 1–6. arXiv Preprint https://people.csail.mit. edu/khosla/papers/arxiv2016_Wang.pdf
Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. arXiv 2015, arXiv:1505.00853
Zhang C, Zhao J, Niu J, Li D (2020) New convolutional neural network model for screening and diagnosis of mammograms. PLoS ONE 15(8):e0237674. https://doi.org/10.1371/journal.pone.0237674
Zhang C, Zhao J, Niu J, Li D (2020) New convolutional neural network model for screening and diagnosis of mammograms. PLoS ONE 15(8):e0237674. https://doi.org/10.1371/journal
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Sridevi, V., Samath, J.A. A combined deep CNN-lasso regression feature fusion and classification of MLO and CC view mammogram image. Int J Syst Assur Eng Manag 15, 553–563 (2024). https://doi.org/10.1007/s13198-023-01871-x
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DOI: https://doi.org/10.1007/s13198-023-01871-x