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CADNet157 model: fine-tuned ResNet152 model for breast cancer diagnosis from mammography images

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Abstract

The risk of death incurred by breast cancer is rising exponentially, especially among women. This made the early breast cancer detection a crucial problem. In this paper, we propose a computer-aided diagnosis (CAD) system, called CADNet157, for mammography breast cancer based on transfer learning and fine-tuning of well-known deep learning models. Firstly, we applied hand-crafted features-based learning model using four extractors (local binary pattern, gray-level co-occurrence matrix, and Gabor) with four selected machine learning classifiers (K-nearest neighbors, support vector machine, random forests, and artificial neural networks). Then, we performed some modifications on the Basic CNN model and fine-tuned three pre-trained deep learning models: VGGNet16, InceptionResNetV2, and ResNet152. Finally, we conducted a set of experiments using two benchmark datasets: Digital Database for Screening Mammography (DDSM) and INbreast. The results of the conducted experiments showed that for the hand-crafted features based CAD system, we achieved an area under the ROC curve (AUC) of 95.28% for DDSM using random forest and 98.10% for INbreast using support vector machine with the histogram of oriented gradients extractor. On the other hand, CADNet157 model (i.e., fine-tuned ResNet152) was the best performing deep model with an AUC of 98.90% (sensitivity: 97.72%, specificity: 100%), and 98.10% (sensitivity: 100%, specificity: 96.15%) for, respectively, DDSM and INbreast. The CADNet157 model overcomes the limitations of traditional CAD systems by providing an early detection of breast cancer and reducing the risk of false diagnosis.

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Availability of data and materials

The DDSM dataset is available online at http://www.eng.usf.edu/cvprg/ Mammography/Database.html. The INbreast dataset can be requested online at http://medicalresearch.inescporto.pt/breastresearch/index.php/Get_INbreast_ Database. The MIAS database is available online at http://peipa.essex.ac.uk/info/mias.html.

Notes

  1. ImageNet challenge is a competition used to measure the performance of CNNs “Large Scale Visual Recognition Challenge”: http://image-net.org/.

References

  1. Islam MM, Haque MR, Iqbal H, Hasan MM, Hasan M, Kabir MN (2020) Breast cancer prediction: a comparative study using machine learning techniques. SN Comput Sci 1(5):1–14

    Article  Google Scholar 

  2. Al-Azzam Nosayba, S Ibrahem (2021) Comparing supervised and semi-supervised machine learning models on diagnosing breast cancer. Annals of Medicine and Surgery

  3. Society, American Cancer(2020) How common is breast cancer? (source: https://www.cancer.org/cancer/breast-cancer/about/how-common-is-breast-cancer.html),

  4. Becker S (2015) A historic and scientific review of breast cancer: The next global healthcare challenge. Int J Gynecol Obstetrics 131:S36–S39

    Article  Google Scholar 

  5. Breast cancer now the research & care charity, Breast cancer facts and statistics 2020 (source: https://https://breastcancernow.org/about-us/media/facts-statistics), (2020)

  6. Sakri SB, Rashid NBA, Zain ZM (2018) Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access 6:29637–29647

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015) Machine learning applications in cancer prognosis and prediction. Comput struct biotechnol j 13:8–17

    Article  Google Scholar 

  9. Shen L, Margolies LR, Rothstein JH, Fluder E, Bride RM, Sieh W (2019) Deep learning to improve breast cancer detection on screening mammography. Sci Rep 9(1):1–12

    Article  Google Scholar 

  10. Hepsağ Pınar Uskaner, Ayşe Özel Selma, Yazıcı Adnan (2017)Using deep learning for mammography classification. In 2017 International Conference on Computer Science and Engineering (UBMK), pages 418–423. IEEE,

  11. Digital database for screening mammography (source: http://www.eng.usf.edu/cvprg/mammography/database.html?fbclid= iwar2tkfk5fwj9rqlazcnbzacec4uotmbvltk6i4zry1locv8ow8do8hxmd2i), (2020)

  12. Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS (2012) Inbreast toward a full-field digital mammographic database. Acad Radiol 19(2):236–248

    Article  Google Scholar 

  13. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  14. Chan H-P, Lo S-CB, Sahiner B, Lam KL, Helvie MA (1995) Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network. Med Phys 22(10):1555–1567

    Article  Google Scholar 

  15. Yuzheng W, Giger ML, Doi K, Vyborny CJ, Schmidt RA, Metz CE (1993) Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology 187(1):81–87

    Article  Google Scholar 

  16. Wei Datong, Sahiner Berkman, Chan Heang-Ping , Petrick Nicholas(1995) Detection of masses on mammograms using a convolution neural network. In 1995 International Conference on Acoustics, Speech, and Signal Processing, volume 5, pages 3483–3486. IEEE,

  17. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90

    Article  Google Scholar 

  18. Zeiler Matthew D, Fergus Rob(2014) Visualizing and understanding convolutional networks. In European conference on computer vision, pages 818–833. Springer,

  19. S Christian , L Wei, J Yangqing, S Pierre, R Scott, A Dragomir, E Dumitru , V Vincent, R Andrew (2015) Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9,

  20. S Karen, Z Andrew(2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556,

  21. H Kaiming , Z Xiangyu (2016) Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778,

  22. Abdelhafiz D, Yang C, Ammar R, Nabavi S (2019) Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinform 20(11):1–20

    Google Scholar 

  23. W Dayong , K Aditya , G Rishab , I Humayun , B Andrew H(2016) Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718,

  24. Russakovsky O, Deng J, Hao S, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115(3):211–252

    Article  MathSciNet  Google Scholar 

  25. Khamparia A, Bharati S, Podder P, Gupta D, Khanna A, Phung TK, Thanh DNH (2021) Diagnosis of breast cancer based on modern mammography using hybrid transfer learning. Multidimension Syst Signal Process 32(2):747–765

    Article  MATH  Google Scholar 

  26. Montaha S, Azam S, Rafid AKMRH, Pronab Ghosh Md, Hasan MJ, De Boer F et al (2021) Breastnet18: a high accuracy fine-tuned vgg16 model evaluated using ablation study for diagnosing breast cancer from enhanced mammography images. Biology 10(12):1347

    Article  Google Scholar 

  27. T Monika , B Rashi , S Praditi , L Reena (2020) Breast cancer prediction using deep learning and machine learning techniques. Available at SSRN 3558786,

  28. Breast cancer wisconsin (original) data set (source: . https:// archive.ics.uci.edu/ml/machine-learning-databases/breast-cance r-wisconsin/breast-cancer-wisconsin.data.)

  29. 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

    Article  Google Scholar 

  30. Safdarian N, Hedyezadeh M (2019) Detection and classification of breast cancer in mammography images using pattern recognition methods. Multidiscip Cancer Investig 3(4):13–24

    Article  Google Scholar 

  31. Al-Antari MA, Han S-M, Kim T-S (2020) Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital x-ray mammograms. Comput Methods Programs Biomed 196:105584

    Article  Google Scholar 

  32. S Tianyu , W Jiangong , G Chao , W FEIYUE (2020) Hierarchical fused model with deep learning and type-2 fuzzy learning for breast cancer diagnosis. IEEE Transactions on Fuzzy Systems,

  33. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  34. Chougrad H, Zouaki H, Alheyane O (2020) Multi-label transfer learning for the early diagnosis of breast cancer. Neurocomputing 392:168–180

    Article  Google Scholar 

  35. Lopez MA Guevara, Posada N, Moura Daniel C, Pollán Raúl Ramos, Valiente José M Franco , Ortega César Suárez, Solar M, Diaz-Herrero Guillermo , Ramos IMAP , Loureiro J, et al. (2012)Bcdr: a breast cancer digital repository. In 15th International conference on experimental mechanics, volume 1215,

  36. J P SUCKLING (1994) The mammographic image analysis society digital mammogram database. Digital Mammo, pages 375–386,

  37. Montaha S, Azam S, Rafid AKMRH, Pronab Ghosh Md, Hasan MJ, De Boer F et al (2021) Breastnet18: a high accuracy fine-tuned vgg16 model evaluated using ablation study for diagnosing breast cancer from enhanced mammography images. Biology 10(12):1347

    Article  Google Scholar 

  38. Google scholar (source: https://scholar.google.com/), (2022)

  39. Ghosh Pronab , Azam Sami , Hasib Khan Md, Karim Asif, Jonkman Mirjam , Anwar Adnan (2021) A performance based study on deep learning algorithms in the effective prediction of breast cancer. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE,

  40. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  41. M Raouia , G Norhene , D Alima , S Dorra , F Wiem , Mnif, Zaineb (2019) A novel cad system for breast dce-mri based on textural analysis using several machine learning methods. In International Conference on Hybrid Intelligent Systems, pages 176–187. Springer,

  42. A Zainab, J Se-In(2019) A convolution-free lbp-hog descriptor for mammogram classification. arXiv preprint arXiv:1904.00187, pages 1–5,

  43. Mokni R, Gargouri N, Damak A, Sellami D, Feki W, Mnif Z (2021) An automatic computer-aided diagnosis system based on the multimodal fusion of breast cancer (mf-cad). Biomed Signal Process Control 69:102914

    Article  Google Scholar 

  44. Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621

    Article  Google Scholar 

  45. Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804

    Article  Google Scholar 

  46. Liu G-H, Yang J-Y (2008) Image retrieval based on the texton co-occurrence matrix. Pattern Recogn 41(12):3521–3527

    Article  MATH  Google Scholar 

  47. M Raouia, M Anis, D Hassen, K Monji (2017) Multiset canonical correlation analysis: texture feature level fusion of multiple descriptors for intra-modal palmprint biometric recognition. In Pacific-Rim Symposium on Image and Video Technology, pages 3–16. Springer,

  48. T Krishna Chaitanya, B Chandra Mohan, S Srinivas Kumar(2018) The analysis of digital mammograms using hog and glcm features. In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pages 1–7. IEEE,

  49. D Navneet , T Bill (2005) Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), volume 1, pages 886–893. Ieee,

  50. Gargouri N (2012) A Dammak Masmoudi, D Sellami Masmoudi, and R Abid. A new glld operator for mass detection in digital mammograms, International journal of biomedical imaging

    Google Scholar 

  51. Mehdi MZ, Ayed NGB, Masmoudi AD, Sellami D (2020) A textural wavelet quantization approach for an efficient breast microcalcifcation’s detection. Multimed Tools and Appl 79(33):24911–24927

    Article  Google Scholar 

  52. Karthiga R, Narashimhan K(2021) Deep convolutional neural network for computer-aided detection of breast cancer using histopathology images. In Journal of Physics: Conference Series, volume 1767, page 012042. IOP Publishing,

  53. Mehra R et al (2018) Breast cancer histology images classification: training from scratch or transfer learning? ICT Express 4(4):247–254

    Article  Google Scholar 

  54. Falconi LG, Perez M, Aguilar WG, Conci A (2020) Transfer learning and fine tuning in breast mammogram abnormalities classification on cbis-ddsm database. Adv Sci Technol Eng Sys 5(2):154–165

    Article  Google Scholar 

  55. S Christian, I Sergey, V Vincent, A Alexander(2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31,

  56. H Kaiming, Z Xiangyu, R Shaoqing, S Jian (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778,

  57. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143(1):29–36

    Article  Google Scholar 

  58. Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N (2021) Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 13(1):1–17

    Article  Google Scholar 

  59. American cancer society, breast cancer early detection and diagnosis, can breast cancer be found early?, (2022)

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Authors and Affiliations

  1. College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia

    Raouia Mokni & Mariem Haoues

  2. University of Sfax, Sfax, Tunisia

    Raouia Mokni

  3. Mir@cl Laboratory, University of Sfax, ISIMS, BP 242. 3021, Sfax, Tunisia

    Mariem Haoues

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  1. Raouia Mokni
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Mokni, R., Haoues, M. CADNet157 model: fine-tuned ResNet152 model for breast cancer diagnosis from mammography images. Neural Comput & Applic 34, 22023–22046 (2022). https://doi.org/10.1007/s00521-022-07648-w

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