Skip to main content

Advertisement

Log in

Anomaly detection model of mammography using YOLOv4-based histogram

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

Breast cancer is the second leading cause of death in females. As such, women have high incidence and mortality rates of breast cancer. The incidence rate has been on the rise over time. The earlier breast cancer is caught, the better it shows prognosis and the lower the mortality rate is. For this reason, many researchers and medical doctors have heeded a lot of attention to the CAD systems to detect and classify breast cancer. They have proposed a myriad of methods and techniques. Among them, the CAD system based on artificial intelligence (AI) can process plenty of information fast, and its performance is evaluated to be high. As an AI algorithm, YOLO has excellent detection performance and can detect objects effectively in real time. In this paper, we proposed an anomaly detection model of mammography using a YOLOv4-based histogram. In terms of breast cancer diagnosis, mammography features a fast diagnosis time and an inexpensive cost. For this reason, it is often applied to breast cancer diagnosis. Mammography, however, generates an image only with brightness values, so that a mammogram image has a lot of noise and image edges are dim. To enhance these image edges, we create a difference through histogram and brightness range control and threshold-based region removal methods and expand the single channel of mammogram images using the generated images. Through the expansion, the image edges are enhanced and converted into a single channel again and are learned through YOLO. For performance evaluation, the method proposed in this study is compared with ResNet18, ResNet50, GoogleNet, and VGG16. According to an experiment, the proposed method had the highest accuracy, or 95.74%, followed by GoogleNet (89.9%), VGG16 (88.93%), ResNet50 (87.77%), and ResNet18 (87.67%) in order.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Hyeon YH, Moon KJ (2020) Cancer care facilities nurses experience of infection control. Journal of Korean Academy of Fundamentals of Nursing 27(1):12–28

    Article  Google Scholar 

  2. Do EH, Choi EJ (2019) The effect of self-efficacy and depression on sense of family coherence in cancer patients undergoing chemotherapy and primary caregivers in day care wards: using the method actor-partner interdependence model. Asian Oncology Nursing 19(4):214–223

    Article  MathSciNet  Google Scholar 

  3. Kwon SY, Kim YJ, Kim GG (2018) An automatic breast mass segmentation based on deep learning on mammogram. Journal of Korea Multimedia Society 21(12):1363–1369

    Google Scholar 

  4. Manohar S, Dantuma M (2019) Current and future trends in photoacoustic breast imaging. Photoacoustics 16:1–27

    Article  Google Scholar 

  5. Lee J, Vicil F (2020) Effects of an evidence-based exercise intervention on clinical outcomes in breast cancer survivors: a randomized controlled trial. The Asian Journal of Kinesiology 22(1):1–8

    Article  Google Scholar 

  6. Kim CH, Park R, Hong E (Oct. 2020) Breast Mass Classification using eLFA Algorithm based on CRNN Deep Learning Model. IEEE Access 8:197312–197323

    Article  Google Scholar 

  7. Natarajan R et al (2020) Environmental exposures during puberty: Window of breast cancer risk and epigenetic damage. Int J Env Res Pub He 17(2):1–17

    Article  MathSciNet  Google Scholar 

  8. Birnbaum JK et al (2018) Early detection and treatment strategies for breast cancer in low-income and upper middle-income countries: a modelling study. Lancet Glob Health 6(8):885–893

    Article  Google Scholar 

  9. Acharya UR et al (2019) A novel algorithm for breast lesion detection using textons and local configuration pattern features with ultrasound imagery. IEEE Access 7:22829–22842

    Article  Google Scholar 

  10. Li Y, Wu J, Wu Q (2019) Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning. IEEE Access 7:21400–21408

    Article  Google Scholar 

  11. Suh YJ, Jung J, Cho BJ (2020) Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning. Journal of personalized medicine 10(4):211

    Article  Google Scholar 

  12. Zheng J, Lin D, Gao Z, Wang S, He M, Fan J (2020) Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis. IEEE Access 8:96946–96954. https://doi.org/10.1109/ACCESS.2020.2993536

    Article  Google Scholar 

  13. Wang D, Khosla A, Gargeya R, Irshad H, Beck AH (2016) Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718

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

    Article  Google Scholar 

  15. Shin DH, Park R, Chung K (June 2020) Decision Boundary-Based Anomaly Detection Model Using Improved AnoGAN From ECG Data. IEEE Access 8:108664–108674

    Article  Google Scholar 

  16. Chung K, Yoo H, Choe DE (Feb. 2019) Ambient context-based modeling for health risk assessment using deep neural network. J Ambient Intell Humaniz Comput 11(4):1387–1395

    Article  Google Scholar 

  17. Lunit Insight MMG System (2021) Accessed: Feb. 02, 2021. [Online]. Available: https://www.lunit.io/.

  18. Genius AI™ Detection Technology (2021) Accessed: Feb. 02, 2021. [Online]. https://www.hologic.com/.

  19. Igarashi S, Sasaki Y, Mikmi T, Skuraba H, Fukuda S (2020) Anatomical classification of upper gastrointestinal organs under various image capture conditions using AlexNet. Comput Biol Med 124. https://doi.org/10.1016/j.compbiomed.2020.103950

  20. Xu P, Chen C, Wang X, Li W, Sun J (2020) ROI-Based Intraoperative MR-CT Registration for Image-Guided Multimode Tumor Ablation Therapy in Hepatic Malignant Tumors. IEEE Access 8:13613–13619

    Article  Google Scholar 

  21. Albahli S, Nida N, Irtaza A, Yousaf MH, Mahmood MT (2020) Melanoma Lesion Detection and Segmentation Using YOLOv4-DarkNet and Active Contour. IEEE Access 8:198403–198414

    Article  Google Scholar 

  22. Shakarami A, Tarrah H, Mahdavi-Hormatc A (2020) A CAD system for diagnosing Alzheimer’s disease using 2D slices and an improved AlexNet-SVM method. Optik 212. https://doi.org/10.1016/j.ijleo.2020.164237

  23. Zeng Z, Xie W, Zhang Y, Lu Y (2019) RIC-Unet: An Improved Neural Network Based on Unet for Nuclei Segmentation in Histology Images. IEEE Access 7:21420–21428

    Article  Google Scholar 

  24. Liang X, Fang J, Li H, Yang X, Ni D, Zeng F, Chen Z (2020) CR-Unet-Based Ultrasonic Follicle Monitoring to Reduce Diameter Variability and Generate Area Automatically as a Novel Biomarker for Follicular Maturity. Ultrasound Med Biol 46(11):3125–3134

    Article  Google Scholar 

  25. Li H, Matsunaga D, Matsui TS, Aosaki H, Deguchi S (2020) Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet. Biochem Biophys Res Commun 503(3):527–532

    Article  Google Scholar 

  26. Moser EC, Narayan G (2020) Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits. Breast 50:25–29

    Article  Google Scholar 

  27. Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A (2019) Dataset of breast ultrasoundimages. Data Brief. https://doi.org/10.1016/j.dib.2019.104863

  28. Paulo SR (2017) Breast ultrasound image. Mendeley data. https://doi.org/10.17632/wmy84gzngw.1

  29. Z. Liu, C. Yang, J. Huang, S. Liu, Y. Zhuo, X. Lua (2021) “Deep learning framework based on integration of S-Mask R-CNN and Inception-v3 for ultrasound image-aided diagnosis of prostate cancer,” 114: 358-367

  30. Chakravarthy SR, Rajaguru H (2021) Automatic Detection and Classification of Mammograms Using Improved Extreme Learning Machine with Deep Learning. IRBM. https://doi.org/10.1016/j.irbm.2020.12.004.

  31. Deepak S, Ameer PM (2020) Retrieval of brain MRI with tumor using contrastive loss based similarity on GoogLeNet encodings. Comput Biol Med 125. https://doi.org/10.1016/j.compbiomed.2020.103993

  32. Wang P, Song Q, Li Y, Lv S, Wang J, Li L, Zhang HH (2020) Cross-task extreme learning machine for breast cancer image classification with deep convolutional features. Biomedical Signal Processing and Control 57. https://doi.org/10.1016/j.bspc.2019.101789

Download references

Acknowledgement

This work was supported by the GRRC program of Gyeonggi province. [GRRC KGU 2020-B03, Industry Statistics and Data Mining Research]

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Roy C. Park.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, CM., Chung, K. & Park, .C. Anomaly detection model of mammography using YOLOv4-based histogram. Pers Ubiquit Comput 27, 1233–1244 (2023). https://doi.org/10.1007/s00779-021-01598-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-021-01598-1

Keywords

Navigation