Abstract
As the most common cancer disease in the world, breast cancer is the main cause of death of female cancer patients. The number of women in China is far greater than that in other countries. Therefore, the absolute death toll is often high. Even a small increase in incidence rate will lead to a severe increase in deaths. Therefore, accurate preoperative diagnosis of benign and malignant breast cancer and the status prediction of various clinical indicators are urgently needed in clinical practice. Deep learning based on neural network has been widely used in diagnosis. Therefore, this paper attempts to explore the influence of deep learning on the auxiliary diagnosis technology of small tumor in mammography. In this paper, 200 cases of breast disease patients in a hospital of our city were taken as the research object, and the artificial neural network model was established. Through the experimental simulation, the results showed that the diagnostic sensitivity, specificity and overall accuracy of BP neural network for test set samples were 95.3%, 96.7 and 96.2%, respectively. The experimental results confirmed its generalization ability. In this paper, the core idea of multi feature kernel hash, combined with a variety of features and deep kernel hash network framework, constructs a new multi feature based deep learning network, which can effectively express the image features of breast tumor, and complete the task of breast micro tumor detection with good performance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Albarqouni S, Baur C, Achilles F, Belagiannis V, Navab N (2016) Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans Med Imaging 35(5):1313–1321
Ba H (2020) Medical sports rehabilitation deep learning system of sports injury based on mri image analysis. J Med Imaging Health Inform 10(5):1091–1097
Bleyer A, Baines C, Miller AB (2016) Impact of screening mammography on breast cancer mortality. Int J Cancer 138(8):2003–2012
Brem RF (2016) Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the japan strategic anti-cancer randomized trial (j-start): a randomised controlled trial. Breast Dis Year Book Q 27(4):270–272
Choi JS, Han BK, Ko EY, Ko ES, Hahn SY, Shin JH et al (2016) Comparison between two-dimensional synthetic mammography reconstructed from digital breast tomosynthesis and full-field digital mammography for the detection of t1 breast cancer. Eur Radiol 26(8):2538–2546
Clauser P, Nagl G, Helbich TH, Pinker-Domenig K, Weber M, Kapetas P et al (2016) Diagnostic performance of digital breast tomosynthesis with a wide scan angle compared to full-field digital mammography for the detection and characterization of microcalcifications. Eur J Radiol 85(12):2161–2168
Freitas-Junior R, Rodrigues D, Corrêa RDS, Peixoto JE, de Oliveira HVCG, Rahal R (2016) Contribution of the unified health care system to mammography screening in Brazil, 2013. Radiologia Brasileira 49(5):305–310
Friedewald SM, Rafferty EA, Rose SL, Durand MA, Plecha DM, Greenberg JS et al (2016) Breast cancer screening using tomosynthesis in combination with digital mammography. Breast Cancer Res Treat 156(1):109–116
Ghesu FC, Krubasik E, Georgescu B, Singh V, Comaniciu D (2016) Marginal space deep learning: efficient architecture for volumetric image parsing. IEEE Trans Med Imaging 35(5):1217–1228
Hatt M, Parmar C, Qi J, Naqa IE (2019) Machine (deep) learning methods for image processing and radiomics. IEEE Trans Radiat Plasma Med Sci 3(2):104–108
He N, Wu Y-P, Kong Y, Lv N, Huang Z-M, Li S et al (2016) The utility of breast cone-beam computed tomography, ultrasound, and digital mammography for detecting malignant breast tumors: a prospective study with 212 patients. Eur J Radiol 85(2):392–403
Karunamuni R, Naha PC, Lau KC, Al-Zaki A, Popov AV, Delikatny EJ et al (2016) Development of silica-encapsulated silver nanoparticles as contrast agents intended for dual-energy mammography. Eur Radiol 26(9):1–9
Kim KI, Lee KH, Kim TR, Chun YS, Lee TH, Choi HY et al (2016) Changing patterns of microcalcification on screening mammography for prediction of breast cancer. Breast Cancer 23(3):471–478
Kim M, Yun J, Cho Y, Shin K, Kim N (2019) Deep learning in medical imaging. Neurospine 16(4):657–668
Klang E (2018) Deep learning and medical imaging. J Thorac Dis 10(3):1325–1328
Kyungsang K, Dufan W, Kuang G et al (2018) Penalized PET reconstruction using deep learning prior and local linear fitting. IEEE Trans Med Imaging 37(6):1478–1487
Lee EH, Kim KW, Kim YJ, Shin DR, Jun JK (2016) Performance of screening mammography: a report of the alliance for breast cancer screening in Korea. Korean J Radiol 17(4):489–496
Morrell S, Taylor R, Roder D, Robson B, Gregory M, Craig K (2017) Mammography service screening and breast cancer mortality in New Zealand: a national cohort study 1999–2011. Br J Cancer 116(6):828–839
Mu CC, Li G (2019) Research progress in medical imaging based on deep learning of neural network. Zhonghua kou qiang yi xue za zhi Zhonghua kouqiang yixue zazhi Chin J Stomatol 54(7):492–497
Rakow-Penner R, Ojeda-Fournier H (2016) Breast mri as an adjunct to mammography for breast cancer screening in high-risk patients: retrospective review. Am J Roentgenol 27(1):41–42
Sharma A, Rani R (2017) An optimized framework for cancer classification using deep learning and genetic algorithm. J Med Imaging Health Inform 7(8):1851–1856
Shivappa N, Harris H, Wolk A, Hebert JR (2016) Association between inflammatory potential of diet and mortality among women in the swedish mammography cohort. Eur J Nutr 55(5):1–10
Sprague BL, Arao RF, Miglioretti DL, Henderson LM, Buist D, Onega T et al (2017) National performance benchmarks for modern screening digital mammography: update from the breast cancer surveillance consortium. Radiology 283(1):59–69
Wanders J, Holland K, Veldhuis WB, Mann RM, Pijnappel RM, Peeters P et al (2017) Volumetric breast density affects performance of digital screening mammography. Breast Cancer Res Treat 162(1):95–103
Weigel S, Heindel W, Heidrich J, Hense HW, Heidinger O (2017) Digital mammography screening: sensitivity of the programme dependent on breast density. Eur Radiol 27(7):2744–2751
Acknowledgements
This work was supported by Heilongjiang education department in 2019,research on accurate diagnosis technology of micro breast tumor based on full digital X-ray mammography images (Project No.2019-KYYWF-1250).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author claims no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, Y., Li, J., Xu, D. et al. Auxiliary diagnosis of small tumor in mammography based on deep learning. J Ambient Intell Human Comput 14, 1061–1069 (2023). https://doi.org/10.1007/s12652-021-03358-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-021-03358-8