Advertisement

Detection of breast cancer via deep convolution neural networks using MRI images

  • Ahmet Haşim Yurttakal
  • Hasan ErbayEmail author
  • Türkan İkizceli
  • Seyhan Karaçavuş
Article
  • 47 Downloads

Abstract

Breast cancer is the type of cancer that develops from cells in the breast tissue. It is the leading cancer in women. Early detection of the breast cancer tumor is crucial in the treatment process. Mammography is a valuable tool for identifying breast cancer in the early phase before physical symptoms develop. To reduce false-negative diagnosis in mammography, a biopsy is recommended for lesions with greater than a 2% chance of having suspected malignant tumors and, among them, less than 30 percent are found to have malignancy. To decrease unnecessary biopsies, recently, Magnetic Resonance Imaging (MRI) has also been used to diagnose breast cancer. MRI is the highly recommended test for detecting and monitoring breast cancer tumors and interpreting lesioned regions since it has an excellent capability for soft tissue imaging. However, it requires an experienced radiologist and time-consuming process. On the other hand, convolutional neural networks (CNNs) have demonstrated better performance in image classification compared to feature-based methods and show promising performance in medical imaging. Herein, CNN was employed to characterize lesions as malignant or benign tumors using MRI images. Using only pixel information, a multi-layer CNN architecture with online data augmentation was designed. Later, the CNN architecture was trained and tested. The accuracy of the network is 98.33% and the error rate 0.0167. The sensitivity of the network is 1.0 whereas specificity is 0.9688. The precision is 0.9655.

Keywords

Breast cancer Convolutional neural network Classification 

Notes

Acknowledgments

This study was not supported by any funding source. The authors declare that they have no conflict of interest. The authors alone are responsible for the content and writing of the paper. All authors read and approved the final manuscript.

Also, we are thankful for the anonymous referees for their valuable comments to improve the quality of the article.

Authors Contribution

Ahmet Haşim Yurttakal and Hasan Erbay designed the model and the computational framework; analyzed the data. Türkan İkizceli and Seyhan Karaçavuş acquired the dataset and analyzed the data. All authors discussed the results and contributed to the final manuscript.

References

  1. 1.
    Adler D, Helvie M (1992) Mammographic biopsy recommendations. Curr Opin Radiol 4(5):123–129Google Scholar
  2. 2.
    Agner SC, Rosen MA, Englander S, Tomaszewski JE, Feldman MD, Zhang P, Mies C, Schnall MD, Madabhushi A (2014) Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced mr images: a feasibility study. Radiology 272(1):91–99CrossRefGoogle Scholar
  3. 3.
    Aydıntuğ S (2004) Meme kanserinde erken tanı. Sted 13(6):226–229Google Scholar
  4. 4.
    Berg WA (2010) Benefits of screening mammography. Jama 303(2):168–169CrossRefGoogle Scholar
  5. 5.
    Bhooshan N, Giger M, Medved M, Li H, Wood A, Yuan Y, Lan L, Marquez A, Karczmar G, Newstead G (2014) Potential of computer-aided diagnosis of high spectral and spatial resolution (hiss) mri in the classification of breast lesions. J Magn Reson Imaging 39(1):59–67CrossRefGoogle Scholar
  6. 6.
    Cady B, Michaelson JS (2001) The life-sparing potential of mammographic screening. Cancer 91(9):1699–1703CrossRefGoogle Scholar
  7. 7.
    Cai H, Liu L, Peng Y, Wu Y, Li L (2014) Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols. BMC Cancer 14(1):366CrossRefGoogle Scholar
  8. 8.
    Carneiro G, Nascimento J, Bradley AP (2015) Unregistered multiview mammogram analysis with pre-trained deep learning models. In: International Conference on Medical image Computing and Computer-assisted Intervention, Springer, pp 652–660Google Scholar
  9. 9.
    Dhungel N, Carneiro G, Bradley AP (2015) Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE, pp 1–8Google Scholar
  10. 10.
    D’Orsi CJ (2013) ACR BI-RADS Atlas: breast imaging reporting and data system. American College of RadiologyGoogle Scholar
  11. 11.
    Feig SA, D’Orsi CJ, Hendrick RE, Jackson VP, Kopans DB, Monsees B, Sickles EA, Stelling CB, Zinninger M, Wilcox-Buchalla P (1998) American college of radiology guidelines for breast cancer screening. AJR Am J Roentgenol 171 (1):29–33CrossRefGoogle Scholar
  12. 12.
    Fonseca P, Mendoza J, Wainer J, Ferrer J, Pinto J, Guerrero J, Castaneda B (2015) Automatic breast density classification using a convolutional neural network architecture search procedure. In: Medical Imaging 2015: Computer-aided Diagnosis, vol 9414. International Society for Optics and Photonics, p 941428Google Scholar
  13. 13.
    Gallego-Ortiz C, Martel AL (2015) Improving the accuracy of computer-aided diagnosis for breast mr imaging by differentiating between mass and nonmass lesions. Radiology 278(3):679–688CrossRefGoogle Scholar
  14. 14.
    Gity M, Arabkheradmand A, Taheri E, Shakiba M, Khademi Y, Bijan B, Sadaghiani MS, Jalali AH (2017) Magnetic resonance imaging features of adenosis in the breast. J Breast Cancer 20(1):116CrossRefGoogle Scholar
  15. 15.
    Gu Q, Zhu L, Cai Z (2009) Evaluation measures of the classification performance of imbalanced data sets. In: International Symposium on Intelligence Computation and Applications, Springer, pp 461–471Google Scholar
  16. 16.
    Gubern-Mérida A, Martí R, Melendez J, Hauth JL, Mann RM, Karssemeijer N, Platel B (2015) Automated localization of breast cancer in dce-mri. Med Image Anal 20(1):265–274CrossRefGoogle Scholar
  17. 17.
    Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R (2018) Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 47:45–67CrossRefGoogle Scholar
  18. 18.
    Hassanien AE, Kim TH (2012) Breast cancer mri diagnosis approach using support vector machine and pulse coupled neural networks. J Appl Log 10(4):277–284MathSciNetCrossRefGoogle Scholar
  19. 19.
    Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31CrossRefGoogle Scholar
  20. 20.
    Hoo-Chang S, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285CrossRefGoogle Scholar
  21. 21.
    Huynh BQ, Li H, Giger ML (2016) Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Image 3(3):034501CrossRefGoogle Scholar
  22. 22.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
  23. 23.
    Kuhl C (2007) The current status of breast mr imaging part i. choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology 244(2):356–378CrossRefGoogle Scholar
  24. 24.
    Kuhl CK, Schrading S, Strobel K, Schild HH, Hilgers RD, Bieling HB (2014) Abbreviated breast magnetic resonance imaging (mri): first postcontrast subtracted images and maximum-intensity projection—a novel approach to breast cancer screening with mri. J Clin Oncol 32(22):2304–2310CrossRefGoogle Scholar
  25. 25.
    Lam T, Nilsson S (2018) Application of convolutional neural networks for fingerprint recognition. Master’s thesis, abc. Master’s Theses in Mathematical SciencesGoogle Scholar
  26. 26.
    LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRefGoogle Scholar
  27. 27.
    Lévy D, Jain A (2016) Breast mass classification from mammograms using deep convolutional neural networks. arXiv:1612.00542
  28. 28.
    Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: recognizing complex activities from sensor data. In: IJCAI, vol 2015, pp 1617–1623Google Scholar
  29. 29.
    Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  30. 30.
    Matthews BW (1975) Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochim Biophys Acta Protein Struct Mol Enzymol 405(2):442–451CrossRefGoogle Scholar
  31. 31.
    Milenković J, Hertl K, Košir A, žibert J, Tasič JF (2013) Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions. Artif Intell Med 58(2):101–114CrossRefGoogle Scholar
  32. 32.
    Özmen V (2008) Breast cancer in the world and turkey. J Breast Health 4 (2):6–12Google Scholar
  33. 33.
    Prince JL, Links JM (2006) Medical Imaging Signals and Systems. Pearson Prentice Hall Upper Saddle River, New JerseyGoogle Scholar
  34. 34.
    Rasti R, Teshnehlab M, Phung SL (2017) Breast cancer diagnosis in dce-mri using mixture ensemble of convolutional neural networks. Pattern Recogn 72:381–390CrossRefGoogle Scholar
  35. 35.
    Retter F, Plant C, Burgeth B, Botella G, Schlossbauer T, Meyer-Bäse A (2013) Computer-aided diagnosis for diagnostically challenging breast lesions in dce-mri based on image registration and integration of morphologic and dynamic characteristics. EURASIP Journal on Advances in Signal Processing 2013(1):157CrossRefGoogle Scholar
  36. 36.
    Roth HR, Farag A, Lu L, Turkbey EB, Summers RM (2015) Deep convolutional networks for pancreas segmentation in ct imaging. In: Medical Imaging 2015: Image Processing, vol 9413. International Society for Optics and Photonics, p 94131gGoogle Scholar
  37. 37.
    Samala RK, Chan HP, Hadjiiski LM, Cha K, Helvie MA (2016) Deep-learning convolution neural network for computer-aided detection of microcalcifications in digital breast tomosynthesis. In: Medical Imaging 2016: Computer-aided Diagnosis, vol 9785. International Society for Optics and Photonics, p 97850yGoogle Scholar
  38. 38.
    Shin J, Tajbakhsh N, Todd Hurst R, Kendall CB, Liang J (2016) Automating carotid intima-media thickness video interpretation with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2526–2535Google Scholar
  39. 39.
    Sickles EA (1991) Periodic mammographic follow-up of probably benign lesions: results in 3,184 consecutive cases. Radiology 179(2):463–468CrossRefGoogle Scholar
  40. 40.
    Sickles EA (1991) Screening for breast cancer with mammography. Clin Imaging 15(4):253–260CrossRefGoogle Scholar
  41. 41.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
  42. 42.
    Smith RA, Cokkinides V, Eyre HJ (2003) American cancer society guidelines for the early detection of cancer, 2003. CA Cancer J Clin 53(1):27–43CrossRefGoogle Scholar
  43. 43.
    Soares F, Janela F, Pereira M, Seabra J, Freire MM (2014) Classification of breast masses on contrast-enhanced magnetic resonance images through log detrended fluctuation cumulant-based multifractal analysis. IEEE Syst J 8(3):929–938CrossRefGoogle Scholar
  44. 44.
    Spick C, Bickel H, Polanec SH, Baltzer PA (2018) Breast lesions classified as probably benign (bi-rads 3) on magnetic resonance imaging: a systematic review and meta-analysis. Eur Radiol 28(5):1919– 1928CrossRefGoogle Scholar
  45. 45.
    Stavros AT, Thickman D, Rapp CL, Dennis MA, Parker SH, Sisney GA (1995) Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. Radiology 196(1):123–134CrossRefGoogle Scholar
  46. 46.
    Waugh S, Purdie C, Jordan L, Vinnicombe S, Lerski R, Martin P, Thompson A (2016) Magnetic resonance imaging texture analysis classification of primary breast cancer. Eur Radiol 26(2):322–330CrossRefGoogle Scholar
  47. 47.
    Weiss WA, Medved M, Karczmar GS, Giger ML (2014) Residual analysis of the water resonance signal in breast lesions imaged with high spectral and spatial resolution (hiss) mri: a pilot study. Medical physics 41(1).  https://doi.org/10.1118/1.4851615 CrossRefGoogle Scholar
  48. 48.
    Wollins DS, Somerfield MR (2008) Q and a: magnetic resonance imaging in the detection and evaluation of breast cancer. J Oncol Pract 4(1):18–23CrossRefGoogle Scholar
  49. 49.
    Xu X, Fu L, Chen Y, Larsson R, Zhang D, Suo S, Hua J, Zhao J (2018) Breast region segmentation being convolutional neural network in dynamic contrast enhanced mri. In: 2018 40Th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp 750–753Google Scholar
  50. 50.
    Yang Q, Li L, Zhang J, Shao G, Zheng B (2015) A new quantitative image analysis method for improving breast cancer diagnosis using dce-mri examinations. Med Phys 42(1):103–109CrossRefGoogle Scholar
  51. 51.
    Yassin NI, Omran S, El Houby EM, Allam H (2017) Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. Computer Methods and Programs in BiomedicineGoogle Scholar
  52. 52.
    Yurttakal AH, Erbay H, İkizceli T, Karaçavuş S, Çınarer S (2018) A comparative study on segmentation and classification in breast mri imaging. The Instute of Integrative Omics and Applied Biotechnology 9(5):23–33Google Scholar
  53. 53.
    Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Trans Image Process 26(7):3142–3155MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Technologies Department, Technical Sciences Vocational SchoolBozok UniversityYozgatTurkey
  2. 2.Computer Engineering Department, Engineering FacultyKırıkkale UniversityKırıkkaleTurkey
  3. 3.Haseki Training and Research Hospital, Department of RadiologyUniversity of Health SciencesİstanbulTurkey
  4. 4.Kayseri Training and Research Hospital, Department of Nuclear MedicineUniversity of Health SciencesKayseriTurkey

Personalised recommendations