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Detection of Breast Cancer in Mammography Using Pretrained Convolutional Neural Networks with Fine-Tuning

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Abstract

Breast cancer is a major health concern for women, especially in Latin America where the incidence and mortality rates are high. Mammography is an essential diagnostic tool in detecting breast cancer, but interpreting mammogram images can be challenging due to their complex nature. To assist radiologists in identifying abnormalities in mammogram images, deep learning algorithms, specifically deep convolutional neural networks (DCNNs), are being employed. This chapter explores the effectiveness of several pretrained DCNN models, such as ResNet-50, ResNet152, VGG19, and EfficientB7, in classifying mammogram images.

To ensure reliable results, the Mini-MIAS and CBIS-DDSM datasets, consisting of 334 and 2620 scanned film mammography images, respectively, were selected for this study. The images were categorized into binary classification and multiclassification groups based on the severity of the lesion. For both datasets, the same preprocessing approach was used to enhance image quality. This involved normalizing the images and applying contrast limited adaptive histogram equalization (CLAHE). The efficacy of the preprocessing techniques was evaluated by comparing the performance of the models on the entire dataset and just the normalized images. Four different stages were tested using images from both datasets, and the performance of each model was evaluated using five metrics, namely, accuracy, precision, recall, F1-score, and area under the ROC curve (AUC).

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References

  1. Villarreal-Garza, C., Aguila, C., Magallanes-Hoyos, M.C., Mohar, A., Bargalló, E., Meneses, A., Cazap, E., Gomez, H., López-Carrillo, L., Chávarri-Guerra, Y., Murillo, R., Barrios, C.: Breast cancer in young women in Latin America: an unmet, growing burden. Oncologia. 18(12), 1298–1306 (2013). https://doi.org/10.1634/theoncologist.2013-0321

    Article  Google Scholar 

  2. Villarreal-Garza, C., Mesa-Chavez, F., Plata de la Mora, A., Miaja-Avila, M., Garcia-Garcia, M., Fonseca, A., de la Rosa-Pacheco, S., Cruz-Ramos, M., García Garza, M.R., Mohar, A., Bargallo-Rocha, E.: Prospective study of fertility preservation in young women with breast cancer in Mexico. J. Natl. Compr. Cancer Netw, 1–8 (2021). https://doi.org/10.6004/jnccn.2020.7692

  3. Chávarri-Guerra, Y., Villarreal-Garza, C., Liedke, P.E., Knaul, F., Mohar, A., Finkelstein, D.M., Goss, P.E.: Breast cancer in Mexico: a growing challenge to health and the health system. Lancet. Oncol. 13(8) (2012). https://doi.org/10.1016/S1470-2045(12)70246-2

  4. Houssein, E.H., Emam, M.M., Ali, A.A., Suganthan, P.N.: Deep and machine learning techniques for medical imaging-based breast cancer: a comprehensive review. Expert Syst. Appl. 167 (2021). https://doi.org/10.1016/j.eswa.2020.114161

  5. Pisano, E.D., Yaffe, M.J.: Digital mammography. Radiol. 234(2), 353–362 (2005). https://doi.org/10.1148/radiol.2342030897

    Article  Google Scholar 

  6. Wang, J., Zhu, H., Wang, S.H., Zhang, Y.D.: A review of deep learning on medical image analysis. Mobile. Netw. Appl. 26, 351–380 (2021). https://doi.org/10.1007/s11036-020-01672-7

    Article  Google Scholar 

  7. Dong, S., Wang, P., Abbas, K.: A survey on deep learning and its applications. Comput. Sci. Rev. 40 (2021). https://doi.org/10.1016/j.cosrev.2021.100379

  8. Zhang, X., Dahu, W.: Application of artificial intelligence algorithms in image processing. J. Vis. Commun. Image Represent. 61, 42–49 (2019). https://doi.org/10.1016/j.jvcir.2019.03.004

    Article  Google Scholar 

  9. Hassabis, D., Kumaran, D., Summerfield, C., Botvinick, M.: Neuroscience-inspired artificial intelligence. Neuron. 95(2), 245–258 (2017). https://doi.org/10.1016/j.neuron.2017.06.011

    Article  Google Scholar 

  10. Bagchi, S., Huong, A.: Signal processing techniques and computer-aided detection systems for diagnosis of breast cancer – a review paper. Ind. J. Sci. Technol. 10(3) (2017). https://doi.org/10.17485/ijst/2017/v10i3/110640

  11. Batchu, S., Liu, F., Amireh, A., Waller, J., Umair, M.: A review of applications of machine learning in mammography and future challenges. Oncologia. 99(8), 483–490 (2021). https://doi.org/10.1159/000515698

    Article  Google Scholar 

  12. Mohanty, A.K., Senapati, M.R., Beberta, S., Lenka, S.K.: Texture-based features for classification of mammograms using decision tree. Neu. Comput. Appl. 23(3–4), 1011–1017 (2013). https://doi.org/10.1007/s00521-012-1025-z

    Article  Google Scholar 

  13. Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., Lew, M.S.: Deep learning for visual understanding: a review. Neurocomputing. 187, 27–48 (2016). https://doi.org/10.1016/j.neucom.2015.09.116

    Article  Google Scholar 

  14. Janiesch, C., Zschech, P., Heinrich, K.: Machine learning and deep learning. Electr. Mark. 31, 685–695 (2021). https://doi.org/10.1007/s12525-021-00475-2

    Article  Google Scholar 

  15. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature. 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  16. Fourcade, A., Khonsari, R.H.: Deep learning in medical image analysis: a third eye for doctors. J. Stomatol. Oral. Maxillofac. Surg. 120(4), 279–288 (2019). https://doi.org/10.1016/j.jormas.2019.06.002

    Article  Google Scholar 

  17. Mamoshina, P., Vieira, A., Putin, E., Zhavoronkov, A.: Applications of deep learning in biomedicine. Mol. Pharm. 13(5), 1445–1454 (2016). https://doi.org/10.1021/acs.molpharmaceut.5b00982

    Article  Google Scholar 

  18. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., et al.: A guide to deep learning in healthcare. Nat. Med. 25(1), 24–29 (2019). https://doi.org/10.1038/s41591-018-0316-z

    Article  Google Scholar 

  19. Lee, R.S., Gimenez, F., Hoogi, A., Miyake, K.K., Gorovoy, M., Rubin, D.L.: A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data. 4 (2017). https://doi.org/10.1038/sdata.2017.177

  20. Suckling, J.P.: The mammographic image analysis society digital mammogram database. Digital. Mammo., 375–386 (1994)

    Google Scholar 

  21. Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., Liu, C.: A survey on deep transfer learning. ICANN. 11141, 270–279 (2018). https://doi.org/10.48550/arXiv.1808.01974

    Article  Google Scholar 

  22. Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Member, S., Xiong, H., He, Q.: A comprehensive survey on transfer learning. Proc. IEEE. 109, 43–76 (2019). https://doi.org/10.48550/arXiv.1911.02685

    Article  Google Scholar 

  23. Falconi, L.G., Perez, M., Aguilar, W.G., Conci, A.: Transfer learning and fine tuning in breast mammogram abnormalities classification on CBIS-DDSM database. Adv. Sci. Technol. Eng. Syst. 5(2), 154–165 (2020). https://doi.org/10.25046/aj050220

    Article  Google Scholar 

  24. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010). https://doi.org/10.1109/TKDE.2009.191

    Article  Google Scholar 

  25. Rampun, A., et al.: Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network. Med. Image Anal. 57, 1–17 (2019). https://doi.org/10.1016/j.media.2019.06.007

    Article  Google Scholar 

  26. Altameem, A., Mahanty, C., Poonia, R.C., Saudagar, A.K.J., Kumar, R.: Breast cancer detection in mammography images using deep convolutional neural networks and fuzzy ensemble modeling techniques. Diagnostics. 12(8), 1812 (2022). https://doi.org/10.3390/diagnostics12081812

    Article  Google Scholar 

  27. Wei, B., Han, Z., He, X., Yin, Y.: Deep Learning Model Based Breast Cancer Histopathological Image Classification, pp. 348–353. Int. Conf. Cloud. Comput. Big. Data. Anal (2017). https://doi.org/10.1109/ICCCBDA.2017.7951937

    Book  Google Scholar 

  28. Auccahuasi, W., Delrieux, C., Sernaqué, F., Flores, E., Moggiano, N.: Detection of Microcalcifications in Digital Mammography Images, Using Deep Learning Techniques, Based on Peruvian Casuistry, pp. 1–4. E-Health. Bioeng. Conf (2019). https://doi.org/10.1109/EHB47216.2019.8969906

    Book  Google Scholar 

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

    Article  Google Scholar 

  30. Charan, S., Khan, M.J., Khurshid, K.: Breast Cancer Detection in Mammograms Using Convolutional Neural Network, pp. 1–5. iCoMET (2018). https://doi.org/10.1109/ICOMET.2018.8346384

    Book  Google Scholar 

  31. Saber, A., Sakr, M., Abo-Seida, O.M., Keshk, A.: Tumor detection and classification in breast mammography-based on fine-tuned convolutional neural networks. Int. J. Comput. Inf. 9(1), 74–84 (2022). https://doi.org/10.21608/IJCI.2021.103605.1063

    Article  Google Scholar 

  32. Qasim, K.R., Ouda, A.J.: An accurate breast cancer detection system based on deep learning CNN. MLU. 20(1), 984–990 (2020). https://doi.org/10.37506/mlu.v20i1.499

    Article  Google Scholar 

  33. Montaha, S., Azam, S., Rafid, A.K.M.R.H., Ghosh, P., Hasan, M.Z., Jonkman, M., De Boer, F.: BreastNet18: a high accuracy fine-tuned VGG16 model evaluated using ablation study for diagnosing breast cancer from enhanced mammography images. Biology. 10(12), 1347 (2021). https://doi.org/10.3390/biology10121347

    Article  Google Scholar 

  34. Allugunti, V.R.: Breast cancer detection based on thermographic images using machine learning and deep learning algorithms. In.t J. Eng. Comput. Sci. 4(1), 56–49 (2022). https://doi.org/10.33545/26633582.2022.v4.i1a.68

    Article  Google Scholar 

  35. Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R., Sieh, W.: Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9(1), 12495 (2019). https://doi.org/10.1038/s41598-019-48995-4

    Article  Google Scholar 

  36. Khamparia, A., Bharati, S., Podder, P., Gupta, D., Khanna, A., Phung, T.K., Thanh, D.N.H.: Diagnosis of breast cancer based on modern mammography using hybrid transfer learning. Multidimens. Syst. Signal. Process. 32(2), 747–765 (2021). https://doi.org/10.1007/s11045-020-00756-7

    Article  MATH  Google Scholar 

  37. Hameed, Z., Zahia, S., Garcia-Zapirain, B., Aguirre, J.J., Vanegas, A.M.: Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors. 20(16), 4373 (2020). https://doi.org/10.3390/s20164373

    Article  Google Scholar 

  38. Khan, H.N., Shahid, A.R., Raza, B., Dar, A.H., Alquhayz, H.: Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access. 7, 165724–165733 (2019). https://doi.org/10.1109/ACCESS.2019.2953318

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. Xi, P., Shu, C., Goubran, R.: Abnormality Detection in Mammography using Deep Convolutional Neural Networks, pp. 1–6. MeMeA (2018). https://doi.org/10.1109/MeMeA.2018.8438639

    Book  Google Scholar 

  41. Hepsa, P.U., Özel, S.A., Yazıcı, A.: Using Deep Learning for Mammography Classification, pp. 418–423. UBMK (2017). https://doi.org/10.1109/UBMK.2017.8093429

    Book  Google Scholar 

  42. Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks For-Large-Scale Image Recognition, pp. 1–14. ICLR (2015). https://doi.org/10.48550/arXiv.1409.1556

    Book  Google Scholar 

  43. He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition, pp. 770–778. CVPR (2016). https://doi.org/10.48550/arXiv.1512.03385

    Book  Google Scholar 

  44. Tan, M., Le, Q.V.: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, pp. 6105–6114. ICML (2019). https://doi.org/10.48550/arXiv.1905.11946

    Book  Google Scholar 

  45. Géron, A.: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems, vol. 2. Shroff Publishers (2019)

    Google Scholar 

  46. Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J., Catanzaro, B., Shelhamer, E.: cuDNN: Efficient Primitives for Deep Learning. ArXiv (2014). https://doi.org/10.48550/arXiv.1410.0759

    Book  Google Scholar 

  47. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going Deeper with Convolutions, pp. 1–9. CVPR (2015). https://doi.org/10.1109/CVPR.2015.7298594

    Book  Google Scholar 

  48. Zuiderveld, K.: Contrast Limited Adaptive Histogram Equalization, pp. 474–485. Graphics Gems (1994). https://doi.org/10.1016/B978-0-12-336156-1.50061-6

    Book  Google Scholar 

  49. Sharma, J., Rai, J.K., Tewari, R.P.: Identification of Pre-processing Technique for Enhancement of Mammogram Images, pp. 115–119. MedCom (2014). https://doi.org/10.1109/MedCom.2014.7005987

    Book  Google Scholar 

  50. Iswardani, A., Hidayat, W.: Mammographic image enhancement using digital image processing technique. IJCSIS. 16(5), 222–226 (2018). https://doi.org/10.48550/arXiv.1806.11496

    Article  Google Scholar 

  51. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big. Data. 6, 60 (2019). https://doi.org/10.1186/s40537-019-0197-0

    Article  Google Scholar 

  52. Oza, P., Sharma, P., Patel, S., Adedoyin, F., Bruno, A.: Image augmentation techniques for mammogram analysis. J. Imaging. 8(5), 141 (2022). https://doi.org/10.3390/jimaging8050141

    Article  Google Scholar 

  53. Oyelade, O.N., Ezugwu, A.E.: A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images. BSPC. 65, 102366 (2021). https://doi.org/10.1016/j.bspc.2020.102366

    Article  Google Scholar 

  54. Wang, J., Perez, L.: The Effectiveness of Data Augmentation in Image Classification Using Deep Learning (2017). https://doi.org/10.48550/arXiv.1712.04621

    Book  Google Scholar 

  55. Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information. 11(2), 125 (2020). https://doi.org/10.3390/info11020125

    Article  Google Scholar 

  56. Russakovsky, O., Deng, J., Su, H., et al.: ImageNet Large Scale Visual Recognition Challenge. Int. J. Comput. Vis. 115, 211–252 (2015). https://doi.org/10.3390/info11020125

    Article  MathSciNet  Google Scholar 

  57. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database, pp. 248–255. CVPR (2009). https://doi.org/10.1109/CVPR.2009.5206848

    Book  Google Scholar 

  58. Kingma, D.P., Lei Ba, J.: ADAM: a method for stochastic optimization. ICLR. (2015). https://doi.org/10.48550/arXiv.1412.6980

  59. Badr, E.A., Joun, C., Nasr, G.E.: Cross Entropy Error Function in Neural Networks: Forecasting Gasoline Demand, pp. 381–384. In FLAIRS-02 Proceedings (2002)

    Google Scholar 

  60. Srivastava, N., Hinton, G., Krizhevsky, A., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. JMLR. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  61. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. ICML. 37, 448–456 (2015). https://doi.org/10.48550/arXiv.1502.03167

    Article  Google Scholar 

  62. Ng, A.Y.: Feature Selection, L1 vs. L2 Regularization, and Rotational Invariance. ICML (2004). https://doi.org/10.1145/1015330.1015435

    Book  Google Scholar 

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Acknowledgments

César Eduardo Muñoz Chavez wants to thank CONACYT for the support of this research. Hermilo Sánchez-Cruz was partially supported by Universidad Autónoma de Aguascalientes, under grant PII22-5. Humberto Sossa thanks CONACYT and IPN under grants FORDECYT-PRONACES 6005 and SIP 20220226 for the financial support.

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Authors and Affiliations

  1. Centro de Ciencias Básicas, Universidad Autónoma de Aguascalientes, Aguascalientes, Mexico

    Cesar Muñoz-Chavez, Hermilo Sánchez-Cruz & Julio Ponce-Gallegos

  2. Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City, Mexico

    Humberto Sossa-Azuela

Authors
  1. Cesar Muñoz-Chavez
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  2. Hermilo Sánchez-Cruz
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  3. Humberto Sossa-Azuela
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  4. Julio Ponce-Gallegos
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Corresponding author

Correspondence to Hermilo Sánchez-Cruz .

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Editors and Affiliations

  1. Information Systems, Autonomous University of Aguascalientes, Aguascalientes, Mexico

    Manuel Mora

  2. Information Technology and Administrative Management, Central Washington University, Ellensburg, WA, USA

    Fen Wang

  3. Informatics, University of Oldenburg, Oldenburg, Germany

    Jorge Marx Gomez

  4. Information Systems, University of Guadalajara, Zapopan, Mexico

    Hector Duran-Limon

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Muñoz-Chavez, C., Sánchez-Cruz, H., Sossa-Azuela, H., Ponce-Gallegos, J. (2024). Detection of Breast Cancer in Mammography Using Pretrained Convolutional Neural Networks with Fine-Tuning. In: Mora, M., Wang, F., Marx Gomez, J., Duran-Limon, H. (eds) Development Methodologies for Big Data Analytics Systems. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-031-40956-1_9

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