Design and implementation of transfer learned deep CNN with feature fusion for automated mammogram classification
Keywords:
transfer learning, CNN, VGG16, LASSO regression, augmentation, mammogram classificationAbstract
Radiologists frequently struggle to define mammography mass lesions, resulting in unneeded breast biopsies to eliminate suspicions, which adds exorbitant costs to an already overburdened patient and medical system..Existing models have limited capability for feature extraction and representation, as well as cancer classification. Therefore, we built deep Convolution neural networks based Computer-aided Diagnosis system to assist radiologists in classifying mammography mass lesions. Here, Two-view LASSO regression feature fusion and fine-tuned transfer learning network model VGG16 were applied for identification of mammogram cancer. First, two independent CNN branches are utilized to extract mammography characteristics from two different perspectives. Feature Extraction is performed by fine-tuning pre-trained deep network models VGG16 which extracts deep convolutional features. Second, the features of the VGG16 models are serially fused using LASSO regression. Lastly, the fused features are entered into the Fully Connected Layer for mammogram classification. The high accuracy of 95.24, senstitivity of 96.11% and AUC score of 97.95% of the proposed approach revealed that it should be used to enhance clinical decision-making.
Downloads
References
The American College of Obstetricians and Gynecologists. Cervical Cancer Screening and Prevention. Obstet. Gynecol. (2016), 168, 1–20.
Yu, X., Pang, W., Xu, Q. et al. Mammographic image classification with deep fusion learning. Sci Rep 10, 14361 (2020). https://doi.org/10.1038/s41598-020-71431-x
Bharati, S., Podder, P., & Mondal, M. R. H. (2020a). Artificial neural network based breast cancer screening: A comprehensive review. International Journal of Computer Information Systems and Industrial Management Applications, 12, 125–137.
Thanh, D., & Surya, P. (2019). A review on CT and X-ray images denoising methods. Informatica, 43(2),151–159.
Khamparia, A., Bharati, S., Podder, P. et al. Diagnosis of breast cancer based on modern mammography using hybrid transfer learning. Multidim Syst Sign Process 32, 747–765 (2021). https://doi.org/10.1007/s11045-020-00756-7
Y. Bengio, Learning deep architectures for AI, found, Trends®Mach. Learn 2 (2009) 1–127, doi: 10.1561/2200000006.
J. Schmidhuber, Deep learning in neural networks: an overview, Neural Netw. 61 (2015) 85–117, doi: 10.1016/j.neunet.2014.09.003.
Y. LeCun, K. Kavukcuoglu, C. Farabet, Convolutional networks and applications in vision, in: Proc. 2010 IEEE Int. Symp. Circuits Syst., 2010, pp. 253–256, doi: 10.1109/ISCAS.2010.5537907.
F. Ciompi, B. de Hoop, S.J. van Riel, K. Chung, E.T. Scholten, M. Oudkerk, P.A. de Jong, M. Prokop, B. van Ginneken, Automatic classification of pulmonary peri- fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box, Med. Image Anal. 26 (2015) 195–202, doi: 10.1016/j.media.2015.08.001.
H.C. Shin, H.R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, R.M. Summers, Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE Trans. Med. Imaging 35 (2016) 1285–1298, doi: 10.1109/TMI.2016.2528162.
W. Peng, R.V. Mayorga, E.M.A. Hussein, An automated confirmatory system for analysis of mammograms, Comput. Methods Programs Biomed. 125 (2016) 134–144, doi: 10.1016/j.cmpb.2015.09.019.
Z. Jiao, X. Gao, Y. Wang, J. Li, A deep feature based framework for breast masses classification, Neurocomputing 197 (2016) 221–231, doi: 10.1016/j. neucom.2016.02.060.
G. Carneiro, J. Nascimento, A.P. Bradley, Unregistered multiview mammogram analysis with pre-trained deep learning models, in: Int. Conf. Med. Image Com- put. Comput.-Assist. Interv., Springer, 2015, pp. 652–660. http://link.springer. com/chapter/10.1007/978- 3- 319- 24574- 4 _ 78.
M. Oquab, L. Bottou, I. Laptev, J. Sivic, Learning and transferring mid-level image representations using convolutional neural networks, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit, 2014, pp. 1717–1724. http://www.cv-foundation.org/openaccess/content _ cvpr _ 2014/html/ Oquab _ Learning _ and _ Transferring _ 2014 _ CVPR _ paper.html . (accessedJanuary 27, 2017).
H. Chen, D. Ni, J. Qin, S. Li, X. Yang, T. Wang, P.A. Heng, Standard plane local- ization in fetal ultrasound via domain transferred deep neural networks, IEEE J. Biomed. Health Inf. 19 (2015) 1627–1636, doi: 10.1109/JBHI.2015.2425041.
Rakhlin, A., Shvets, A., Iglovikov, V., & Kalinin, A. A. (2018). Deep convolutional neural networks for breast cancer histology image analysis. In Paper presented at the international conference image analysis and recognition
Chougrad H, Zouaki H, Alheyane O. Deep convolutional neural networks for breast cancer screening. Computer Methods and Programs in Biomedicine. 2018; 157:19–30. https://doi.org/10.1016/j.cmpb.2018.01.011 PMID: 29477427.
Wang, J. et al. Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci. reports 6, 1–9 (2016).
Li, B., Ge, Y., Zhao, Y., Guan, E. & Yan, W. Benign and malignant mammographic image classification based on convolutional neural networks. In Proceedings of the 2018 10th International Conference on Machine Learning and Computing, 247–251 (2018).
Huynh, B. Q., Li, H. & Giger, M. L. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J. Med. Imaging 3, 034501 (2016).
V. Sridevi, Dr. J. Abdul Samath. (May-June 2020). Advancement on Breast Cancer Detection Using Medio-Lateral-Oblique (Mlo) and Cranio-Caudal (CC) Features. Test Engineering and Management, Vol. 83. pp. 85-93.
Cai, H. et al. Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms. Comput. Math. Methods Med. 1, 4. https ://doi.org/10.1155/2019/27174 54 (2019).
G. Carneiro, J. Nascimento, and A. P. Bradley, Unregistered multiview mammogram analysis with pre-trained deep learning models, in Medical Image Computing And Computer-Assisted Intervention, pp. 652–660, Springer International Publishing Ag, Cham, Switzerland, 2015.
V. Sridevi, A. Nirmala. Inspection of Welding Images Using Image Segementation Techniques, International Journal of Engineering Research & Technology, Vol. 2 Issue 3, March - 2013
Z. Wang, M. Li, H. Wang et al., Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features, Institute of Electrical and Electronics Engineers Access, vol. 7, pp. 105146–105158, 2019.
Z. He, W. Lyu, G. Qin et al., “A feasibility study of building up deep learning classification model based on breast digital breast tomosynthesis image texture feature extraction of the simple mass lesions,” Chinese Journal of Radiology, vol. 52, no. 9, pp. 668–672, 2018.
Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer P. The digital database for screening mammography. Proceedings of the 5th International Workshop on Digital Mammography; 2000 Jun 11–14; Toronto, Canada. Medical Physics Publishing; 2000; pp.212-218.
Breuel, T.M. Efficient Binary and Run Length Morphology and its Application to Document Image Processing. arXiv 2007, arXiv:0712.0121.
Gong, X.Y.; Su, H.; Xu, D.; Zhang, Z.T.; Shen, F.; Yang, H. Bin an Overview of Contour Detection Approaches. Int. J. Autom. Comput. 2018, 15, 656–672.
Dhar, P. A, Method to Detect Breast Cancer Based onMorphological Operation. Int. J. Educ. Manag. Eng. 2021, 11, 25–31.
Hassan, N.; Ullah, S.; Bhatti, N.; Mahmood, H.; Zia, M. The Retinex based improved underwater image enhancement. Multimed. Tools Appl. 2021, 80, 1839–1857.
Halevy, P. Norvig, F. Pereira, The unreasonable effectiveness of data, IEEE Intell. Syst. 24 (2009) 8–12, doi: 10.1109/MIS.2009.36.
S.C. Wong, A. Gatt, V. Stamatescu, M.D. McDonnell, Understanding data aug- mentation for classification: when to warp?, ArXiv160908764 Cs. (2016). http: //arxiv.org/abs/1609.08764.
J. Ma, P. C. Yuen, and J.-H. Jian-Huang Lai, Linear dependency modeling for classifier fusion and feature combination, Institute of Electrical and Electronics Engineers Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 5, pp. 1135–1148, 2013.
Ogutu, J., Schulz-Streeck, T., & Piepho, H.-P. (2012). Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proceedings, 6(Suppl 2), S10.
S. Aydin, N. Arica, E. Ergul et al., Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements, International Journal of Neural Systems, vol. 25, no. 3, 2015
Rusiecki, A. Trimmed categorical cross-entropy for deep learning with label noise. Electron. Lett. 2019, 55, 319. [CrossRef]
D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, ArXiv14126980 Cs. (2014). http://arxiv.org/abs/1412.6980 (accessed January 31,2017).
Xu, T.; Feng, Z.-H.; Wu, X.-J.; Kittler, J. Learning adaptive discrimina-tive correlation _lters via temporal consistency pre-serving spatial feature selection for robust visual object tracking. IEEE Trans. Image Process. 2019, 28, 5596–5609. [CrossRef] [PubMed]
Srivastava , G.E. Hinton , A. Krizhevsky , I. Sutskever , R. Salakhutdinov , Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15 (2014) 1929–1958 .
Suryasa, W., Sudipa, I. N., Puspani, I. A. M., & Netra, I. (2019). Towards a Change of Emotion in Translation of Kṛṣṇa Text. Journal of Advanced Research in Dynamical and Control Systems, 11(2), 1221-1231.
Suwija, N., Suarta, M., Suparsa, N., Alit Geria, A.A.G., Suryasa, W. (2019). Balinese speech system towards speaker social behavior. Humanities & Social Sciences Reviews, 7(5), 32-40. https://doi.org/10.18510/hssr.2019.754
Mustafa, A. R., Ramadany, S., Sanusi, Y., Made, S., Stang, S., & Syarif, S. (2020). Learning media applications for toddler midwifery care about android-based fine motor development in improving midwifery students skills. International Journal of Health & Medical Sciences, 3(1), 130-135. https://doi.org/10.31295/ijhms.v3n1.290
Published
How to Cite
Issue
Section
Copyright (c) 2022 International journal of health sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the International Journal of Health Sciences (IJHS) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJHS right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.
Articles published in IJHS can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
This copyright notice applies to articles published in IJHS volumes 4 onwards. Please read about the copyright notices for previous volumes under Journal History.
