Advertisement

Images data practices for Semantic Segmentation of Breast Cancer using Deep Neural Network

  • 79 Accesses

Abstract

Image data in healthcare is playing a vital role. Medical data records are increasing rapidly, which is beneficial and detrimental at the same time. Large Image dataset are difficult to handle, extracting information, and machine learning. The mammograms data used in this research are low range x-ray images of the breast region, which contains abnormalities. Breast cancer is the most frequently diagnosed cancer and ranked 9th worldwide in breast cancer-related deaths. In Pakistan 1 in 9 women expected to have breast cancer at some stage in life. Screening mammography is the most effective means for its early detection. This high rate of oversampling is responsible for billions in excess health care cost and unnecessary patient anxiety. This research mainly focuses on the development of deep learning based computer-aided system to detect, classify and segment the cancerous region in mammograms. Moreover, the preprocessing mechanism is proposed that remove noise, artifacts and muscle region that can cause a high false positive rate. In order to increase the efficiency of the system and counter the large resource requirement, the pre-processed image is converted to 512 × 512 patches. The two publicly available breast cancer dataset are employed i.e. Mammographic Image Analysis Society (MIAS) digital mammogram dataset and Curated Breast Imaging Subset of (Digital Database for Screening Mammography) (CBIS-DDSM). The two states of art deep learning-based instance segmentation frameworks are used, i.e. DeepLab and Mask RCNN. The pre-processing algorithm helps to increase the area under the receiver operating curve for each transfer learning method. The fine tuning is performed for better performance, the area under the curve was equal to 0.98 and 0.95 for mask RCNN and deep lab respectively on a test set of 150 cases. However, mean average precision for the segmentation task is 0.80 and 0.75. The radiologists accuracy ranged from 0.80 to 0.88. The proposed research has the potential to help radiologists with breast mass classification as well as segmentation of the cancerous region.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. Al-masni MA et al (2018) Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Methods Programs Biomed 157:85–94

  2. Benzebouchi NE, Azizi N, Ayadi K (2019) A computer-aided diagnosis system for breast cancer using deep convolutional neural networks. In: Computational intelligence in data mining. Advances in intelligent systems and computing, vol 711. Springer, Singapore

  3. Bleyer A, Baines C, Miller AB (2016) Impact of screening mammography on breast cancer mortality. Int J Cancer 138:2003–2012

  4. Castellino RA (2005) Computer aided detection (CAD): an overview. Cancer Imaging 5(1):17

  5. Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587

  6. Chougrad H, Zouaki H, Alheyane O (2018) Deep convolutional neural networks for breast cancer screening. Comput Methods Programs Biomed 157:19–30

  7. Clark K et al (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045–1057

  8. Costa AC, Oliveira HC, Catani JH, de Barros N, Melo CF, Vieira MA (2019) Detection of architectural distortion with deep convolutional neural network and data augmentation of limited dataset. In: XXVI Brazilian congress on biomedical engineering. IFMBE proceedings, vol 70/2. Springer, Singapore

  9. de Lima SM, da Silva-Filho AG, dos Santos WP (2016) Detection and classification of masses in mammographic images in a multi-kernel approach. Comput Methods Programs Biomed 134:11–29

  10. Elmoufidi A, El Fahssi K, Jai-Andaloussi S, Sekkaki A, Quellec G, Lamard M, Cazuguel G (2016) Automatic detection of suspicious lesions in digital X-ray mammograms. In: International symposium on ubiquitous networking. Springer, Singapore, pp 375–385

  11. Gedik N (2016) A new feature extraction method based on multi-resolution representations of mammograms. Appl Soft Comput 44:128–133

  12. Giger ML, Chan HP, Boone J (2008) Anniversary paper: history and status of CAD and quantitative image analysis: the role of medical physics and AAPM. Med Phys 35:5799–5820

  13. Gur D et al (2004) Computer-aided detection performance in mammographic examination of masses: assessment. Radiology 233:418–423

  14. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp 2961–2969

  15. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554

  16. Iqbal MM, Mehmood MT, Jabbar S, Khalid S, Ahmad A, Jeon G (2018) An enhanced framework for multimedia data: green transmission and portrayal for smart traffic system. Comput Electr Eng 67:291–308

  17. Jalalian A, Mashohor S, Mahmud R, Karasfi B, Saripan MI, Ramli AR (2017) Computer-assisted diagnosis system for breast cancer in computed tomography laser mammography (CTLM). J Digit Imaging 30:796–811

  18. Kendall EJ, Barnett MG, Chytyk-Praznik K (2013) Automatic detection of anomalies in screening mammograms. BMC Med Imaging 13:43

  19. Khan S, Hussain M, Aboalsamh H, Mathkour H, Bebis G, Zakariah M (2016) Optimized Gabor features for mass classification in mammography. Appl Soft Comput 44:267–280

  20. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

  21. Noble M, Bruening W, Uhl S, Schoelles K (2009) Computer-aided detection mammography for breast cancer screening: systematic review and meta-analysis. Arch Gynecol Obstet 279:881–890

  22. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087

  23. Sonar P, Bhosle U, Choudhury C (2017) Mammography classification using modified hybrid SVM-KNN. In: 2017 international conference on signal processing and communication (ICSPC). IEEE, pp 305–311

  24. Suhail Z, Sarwar M, Murtaza K (2015) Automatic detection of abnormalities in mammograms. BMC Med Imaging 15:53

  25. Swiderski B, Osowski S, Kurek J, Kruk M, Lugowska I, Rutkowski P, Barhoumi W (2017) Novel methods of image description and ensemble of classifiers in application to mammogram analysis. Expert Syst Appl 81:67–78

  26. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1–9

  27. Taghanaki SA, Kawahara J, Miles B, Hamarneh G (2017) Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification. Comput Methods Progr Biomed 145:85–93

  28. Talbar MMPSN (2016) Genetic fuzzy system (GFS) based wavelet. Energy 1:1

  29. Uppal MTN (2016) Classification of mammograms for breast cancer detection using fusion of discrete cosine transform and discrete wavelet transform features. Biomed Res 27(2):322–327

  30. Wang Z, Yu G, Kang Y, Zhao Y, Qu Q (2014) Breast tumor detection in digital mammography based on extreme learning machine. Neurocomputing 128:175–184

  31. Zhang X, Zhang Y, Han EY, Jacobs N, Han Q, Wang X, Liu J (2018) Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Trans Nanobioscience 17:237–242

Download references

Author information

Correspondence to Muhammad Munwar Iqbal.

Ethics declarations

Conflict of interest

All the authors declare that there does not exist any conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ahmed, L., Iqbal, M.M., Aldabbas, H. et al. Images data practices for Semantic Segmentation of Breast Cancer using Deep Neural Network. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-01680-1

Download citation

Keywords

  • Computer aided detection
  • Deep learning
  • Feature extraction
  • Neural network
  • Breast cancer
  • Cancer prediction