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DenseNet for Breast Tumor Classification in Mammographic Images

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Bioengineering and Biomedical Signal and Image Processing (BIOMESIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12940))

Abstract

Breast cancer screening is an efficient method to detect breast lesions early. The common screening techniques are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload for pathologists, and hence is prone to diagnostic errors. Thus, the aim of this study was to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoIAlign) method was developed to automate RoI segmentation. Then feature extraction, selection and classification were carried out by the DenseNet architecture. Finally, the precision and accuracy of the model was evaluated by the AUC, accuracy and precision metrics. To summarize, the findings of this study show that the methodology may improve the diagnosis and efficiency in automatic tumor localization through medical image classification.

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References

  1. Ferlay, J., et al.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer. 136(5), E359–E386 (2015)

    Google Scholar 

  2. Ragab, D.A., Sharkas, M., Marshall, S., Ren, J.: Breast cancer detection using deep convolutional neural networks and support vector machines. Peer J. 7, e6201 (2019)

    Google Scholar 

  3. Shieh, S.H., Hsieh, V.C.R., Liu, S.H., Chien, C.R., Lin, C.C., Wu, T.N.: Delayed time from first medical visit to diagnosis for breast cancer patients in Taiwan. J. Formos. Med. Assoc. 113(10), 696–703 (2014)

    Article  Google Scholar 

  4. Nahid, A.A., Kong, Y.: Involvement of machine learning for breast cancer image classification: a survey. Comput. Math. Methods Med. 2017, 29 (2017). https://doi.org/10.1155/2017/3781951

  5. Bardou, D., Zhang, K., Ahmad, S.M.: Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access 6, 24680–24693 (2018)

    Article  Google Scholar 

  6. Skandalakis, J.E.: Embryology and anatomy of the breast. In: Shiffman, M. (eds) Breast Augmentation, pp. 3–24. Springer, Berlin, Heidelberg (2009). https://doi.org/10.1007/978-3-540-78948-2_1

  7. Huang, Y.L., Chen, D.R., Lin, Y.C.: 3D Contouring for Breast Tumor in Sonography. arXiv preprint arXiv:1901.09407 (2019)

  8. Al Rahhal, M.M.: Breast cancer classification in histopathological images using convolutional neural network. Int. J. Adv. Comput. Sci. Appl. 9(3), 64–68 (2018)

    Google Scholar 

  9. Lim, C.N., Suliong, C., Rao, C.V., et al.: Recent advances in breast cancer diagnosis entering an era of precision medicine. Borneo J. Med. Sci. (BJMS) 13(1), 3–9 (2019)

    Google Scholar 

  10. Karthiga, R., Narasimhan, K.: Automated diagnosis of breast cancer using wavelet based entropy features. In: Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 274–279. IEEE, Coimbatore, India (2018). https://doi.org/10.1109/ICECA.2018.8474739.

  11. Han, Z., Wei, B., Zheng, Y., Yin, Y., Li, K., Li, S.: Breast cancer multi-classification from histopathological images with structured deep learning model. Sci. Rep. 7(1), 1–10 (2017)

    Article  Google Scholar 

  12. Xie, J., Liu, R., Luttrell IV, J., Zhang, C.: Deep learning based analysis of histopathological images of breast cancer. Front. Gene. 10(80), 19 (2019). https://doi.org/10.3389/fgene.2019.00080

  13. Toğaçar, M., Özkurt, K.B., Ergen, B., Cömert, Z.: BreastNet: a novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Physica A: Stat. Mech. App. 545,123592 (2020)

    Google Scholar 

  14. Pan, Y., et al.: Brain tumor grading based on neural networks and convolutional neural networks. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 699–702. IEEE, Milan, Italy (2015)

    Google Scholar 

  15. Camacho-Piedra, C., Espíndola-Zarazúa, V.: Actualización de la nomenclatura BI-RADS® por mastografía y ultrasonido. Anales de Radiología, (México). 17(2), 100–108 (2018)

    Google Scholar 

  16. Huang, Y., Han, L., Dou, H., et al.: Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images. BioMed. Eng. OnLine 18, 8 (2019). https://doi.org/10.1186/s12938-019-0626-5

    Article  Google Scholar 

  17. Liberman, L., Menell, J.H.: Breast imaging reporting and data system (BI-RADS). Radiol. Clin. 40(3), 409–430 (2002)

    Article  Google Scholar 

  18. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708. IEEE, Honolulu, Hawaii (2017)

    Google Scholar 

  19. Kerlikowske, K., et al.: Performance of screening mammography among women with and without a first-degree relative with breast cancer. Ann. Internal Med. 133(11), 855–863 (2000)

    Google Scholar 

  20. Cao, Z., Duan, L., Yang, G., Yue, T., Chen, Q.: An experimental study on breast lesion detection and classification from ultrasound images using deep learning architec-tures. BMC Med. Imaging, 19(51), 9 (2019). https://doi.org/10.1186/s12880-019-0349-x

  21. Duggento, A., et al.: An Ad Hoc random initialization deep neural network architecture for discriminating malignant breast cancer lesions in mammographic images. Contrast Media Mol. Imaging, 2019, 5982834 (2019). https://doi.org/10.1155/2019/5982834

  22. Munir, K., Elahi, H., Ayub, A., Frezza, F., Rizzi, A.: Cancer diagnosis using deep learning: a bibliographic review. Cancers, 11(9), 1235, (2019). https://doi.org/10.3390/cancers11091235

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

    Article  Google Scholar 

  24. Das, K., Conjeti, S., Roy, A.G., Chatterjee, J., Sheet, D.: Multiple instances learning of deep convolutional neural networks for breast histopathology whole slide classification. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 578–581. IEEE, Washington, USA (2018)

    Google Scholar 

  25. Chiao, J.Y., et al.: Detection and classification the breast tumors using mask R-CNN on sonograms. Medicine. 98(19), e15200 (2019)

    Google Scholar 

  26. Jiang, Y., Chen, L., Zhang, H., Xiao, X.: Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PloS ONE. 14(3), e0214587 (2019)

    Google Scholar 

  27. Jiménez-Gaona, Y., Rodríguez-Álvarez, M.J., Lakshminarayanan, V.: Deep-learning-based computer-aided systems for breast cancer imaging: a critical review. Appl. Sci. 10(22), 8298 (2020). https://doi.org/10.3390/app10228298

  28. Duraisamy, S., Emperumal, S.: Computer-aided mammogram diagnosis system using deep learning convolutional fully complex-valued relaxation neural network classifier. IET Comput. Vision 11(8), 656–662 (2017)

    Article  Google Scholar 

  29. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Google Scholar 

  30. Castillo, D., Lakshminarayanan, V., Rodríguez-Álvarez, M.J.: MRI images, brain lesions and deep learning appl. Science 11, 1675 (2021). https://doi.org/10.3390/app11041675

    Article  Google Scholar 

  31. Ravì, D., et al.: Deep learning for health informatics. IEEE J. Biomed. Health Inform. 21(1), 4–21 (2016)

    Article  Google Scholar 

  32. Mohsen, H., El-Dahshan, E.S.A., El-Horbaty, E.S.M., Salem, A.B.M.: Classification using deep learning neural networks for brain tumors. Future Comput. Inf. J. 3(1), 68–71 (2018)

    Article  Google Scholar 

  33. Matta, S.: Various image segmentation techniques. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 5(6), 7536–7539 (2014)

    Google Scholar 

  34. Zhou, Z., Wu, W., Wu, S., Tsui, P.-H., Lin, C.-C., Zhang, L., et al.: Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts. Ultrasound Imaging 36(4), 256–276 (2014)

    Article  Google Scholar 

  35. Levman, J., Warner, E., Causer, P., Martel, A.: Semi-automatic region-of-interest segmentation based computer-aided diagnosis of mass lesions from dynamic contrast-enhanced magnetic resonance imaging based breast cancer screening. J. Digit. Imaging 27(5), 670–678 (2014)

    Article  Google Scholar 

  36. Yap, M.H., et al.: Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J. Biomed. Health Inform. 22(4), 1218–1226 (2017)

    Google Scholar 

  37. Cheng, B., Ran, L., Chou, Y.H., Cheng, J.Z.: Boundary regularized convolutional neural network for layer parsing of breast anatomy in automated whole breast ultrasound. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing, Cham, pp. 259–266 (2017). ISBN 978–3–319–66179–7

    Google Scholar 

  38. Huynh, B., Drukker, K., Giger, M.: MO-DE-207B-06: computer-aided diagnosis of breast ultrasound images using transfer learning from deep convolutional neural networks. Med. Phys. 243(6), 3705 (2016)

    Google Scholar 

  39. Nahid, A.A., Mehrabi, M.A., Kong, Y.: Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. Biomed. Res. Int. 2018, 2362108 (2018). https://doi.org/10.1155/2018/2362108

    Article  Google Scholar 

  40. Ragab, D.A., Sharkas, M., Marshall, S., Ren, J.: Breast cancer detection using deep convolutional neural networks and support vector machines. Peer J. 7, e6201 (2019). https://doi.org/10.7717/peerj.6201

    Article  Google Scholar 

  41. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR, (2015). arXiv preprint arXiv:1409.1556 (2014)

  42. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90.

  43. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2261–2269 (2017). https://doi.org/10.1109/CVPR.2017.243

  44. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  45. Lopez, M.G., et al.: BCDR: a breast cancer digital repository. In: 15th International Conference on Experimental Mechanics, Porto, Portugal, vol. 1215, pp.1–5 (2012). https://bcdr.eu/

  46. Marcomini, K.D., Carneiro, A.A., Schiabel, H.: Application of artificial neural network models in segmentation and classification of nodules in breast ultrasound digital images. Int. J. Biomed. Imaging. 2016, 13 (2016). https://doi.org/10.1155/2016/7987212

  47. Al-Masni, M.A., et al.: 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 (2018)

    Google Scholar 

  48. Debelee, T.G., Schwenker, F., Ibenthal, A., Yohannes, D.: Survey of deep learning in breast cancer image analysis. Evol. Syst. 11(1), 143–163 (2019). https://doi.org/10.1007/s12530-019-09297-2

    Article  Google Scholar 

  49. Ahmed, A.H., Salem, M.A.M.: Mammogram-Based cancer detection using deep convolutional neural networks. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), pp. 694–699. IEEE, Egypt (2018). https://doi.org/10.1109/ICCES.2018.8639224

  50. Prabhakar, T., Poonguzhali, S.: Automatic detection and classification of benign and malignant lesions in breast ultrasound images using texture morphological and fractal features. In: 2017 10th Biomedical Engineering International Conference (BMEiCON), pp. 1–5. IEEE, Japan (2017)

    Google Scholar 

  51. Alkhaleefah, M., Ma, S.C., Chang, Y.L., Huang, B., Chittem, P.K., Achhannagari, V.P.:https://doi.org/10.3390/app10113999 Double-shot transfer learning for breast cancer classification from X-ray images. Appl. Sci. 10(11), 3999 (2020).

    Article  Google Scholar 

  52. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  53. Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep Networks with Stochastic Depth. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9908. Springer, Cham. (2016). https://doi.org/10.1007/978-3-319-46493-0_39https://doi.org/10.1007/978-3-319-46493-0_39

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Acknowledgement

VL would like to thank the natural sciences and engineering research council of Canada (NSERC) for a discovery grant. Y.J.G. and D.C.M. acknowledge the research support of Universidad Técnica Particular de Loja through the project PROY_INV_QUI_2020_2784 and the CSIC grant PTA2019–017113-1/AEI/https://doi.org/10.13039/501100011033.

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Authors and Affiliations

  1. Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP1101608, Loja, Ecuador

    Yuliana Jiménez Gaona & Darwin Castillo Malla

  2. Imagen Molecular i3M, Universitat Politècnica de València (UPV), 46022, Valencia, Spain

    Yuliana Jiménez Gaona, María José Rodriguez-Alvarez, Hector Espino-Morato & Darwin Castillo Malla

  3. Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, Waterloo, Canada

    Yuliana Jiménez Gaona, Darwin Castillo Malla & Vasudevan Lakshminarayanan

  4. Department of Systems Design Engineering, Physics, and Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, N2L3G1, Canada

    Vasudevan Lakshminarayanan

  5. Consejo Superior de Investigaciones Científicas (CSIC), 46022, Valencia, Spain

    Hector Espino-Morato

Authors
  1. Yuliana Jiménez Gaona
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  2. María José Rodriguez-Alvarez
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  3. Hector Espino-Morato
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  4. Darwin Castillo Malla
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  5. Vasudevan Lakshminarayanan
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Corresponding authors

Correspondence to Yuliana Jiménez Gaona or Vasudevan Lakshminarayanan .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Daniel Castillo-Secilla

  3. University of Granada, Granada, Spain

    Luis Javier Herrera

  4. Universidad de Granada, Granada, Granada, Spain

    Héctor Pomares

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Jiménez Gaona, Y., Rodriguez-Alvarez, M.J., Espino-Morato, H., Castillo Malla, D., Lakshminarayanan, V. (2021). DenseNet for Breast Tumor Classification in Mammographic Images. In: Rojas, I., Castillo-Secilla, D., Herrera, L.J., Pomares, H. (eds) Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021. Lecture Notes in Computer Science(), vol 12940. Springer, Cham. https://doi.org/10.1007/978-3-030-88163-4_16

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  • DOI: https://doi.org/10.1007/978-3-030-88163-4_16

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  • Online ISBN: 978-3-030-88163-4

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