LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,187
تعداد مقالات10,817
تعداد مشاهده مقاله72,741,248
تعداد دریافت فایل اصل مقاله12,859,099
Antony Sami, Joy, Arumugam, Umamakeswari. (1402). An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer. سامانه مدیریت نشریات علمی, 15(1), 40-51. doi: 10.30476/mejc.2023.97317.1857
Joy Christy Antony Sami; Umamakeswari Arumugam. "An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer". سامانه مدیریت نشریات علمی, 15, 1, 1402, 40-51. doi: 10.30476/mejc.2023.97317.1857
Antony Sami, Joy, Arumugam, Umamakeswari. (1402). 'An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer', سامانه مدیریت نشریات علمی, 15(1), pp. 40-51. doi: 10.30476/mejc.2023.97317.1857
Antony Sami, Joy, Arumugam, Umamakeswari. An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer. سامانه مدیریت نشریات علمی, 1402; 15(1): 40-51. doi: 10.30476/mejc.2023.97317.1857

An Ensemble Deep Learning Model for the Detection and Classification of Breast Cancer

Middle East Journal of Cancer
مقاله 5، دوره 15، شماره 1 - شماره پیاپی 57، فروردین 2024، صفحه 40-51    اصل مقاله (4.19 M)
نوع مقاله: Original Article(s)
شناسه دیجیتال (DOI): 10.30476/mejc.2023.97317.1857
نویسندگان
Joy Christy Antony Sami* 1؛ Umamakeswari Arumugam2
1Department of Computer Science, School of Computing, SASTRA Deemed to be University, Thanjavur, India
2School of Computing, SASTRA Deemed to be University, Thanjavur, India
چکیده
Background: Detecting breast cancer in its early stages remains a significant challenge in the present context and is a leading cause of death among women, primarily due to delayed identification. This paper presents a practical and accurate approach based on deep learning to identify breast cancer in cytology images.
Method: The analytical approach leverages knowledge from a related problem through a technique known as transfer learning. Convolutional neural networks (CNNs) are employed due to their remarkable performance on large datasets. Image classification architectures such as Google network (GoogleNet), Visual geographical group network (VGGNet), residual network (ResNet), and dense convolution network (DenseNet) are utilized in this approach. By applying transfer learning, the images are classified into two categories: those containing cancer cells and those without them. The performance of the proposed ensemble method is evaluated using a breast cytology image dataset.
Results: The results of our proposed ensemble framework outperform conventional CNN models in terms of precision, recall, and F1 measures, achieving an impressive 86% prediction accuracy. Visual representations of validation graphs for each classifier demonstrate that the ensemble framework surpasses the performance of pre-trained CNN architectures.
Conclusion: Combining the outcomes of conventional CNN architectures into an ensemble framework enhances early breast cancer detection, leading to a reduction in mortality through timely medical interventions.
کلیدواژه‌ها
Biopsy؛ Mammography؛ Machine learning؛ Cytology؛ Deep learning
مراجع
  1. West D, West V. Model selection for a medical diagnostic decision support system: a breast cancer detection case. Artif Intell Med. 2000;20(3):183-204. doi: 10.1016/s0933-3657(00)00063-4.
  2. Barba D, León-Sosa A, Lugo P, Suquillo D, Torres F, Surre F, et al. Breast cancer, screening and diagnostic tools: All you need to know. Crit Rev Oncol Hematol. 2021;157:103174. doi: 10.1016/j.critrevonc.2020.103174.
  3. Wang F, Zhang S, Henderson LM. Adaptive decision-making of breast cancer mammography screening: a heuristic-based regression model. Omega. 2018;76(C):70-84. doi: 10.1016/j.omega.2017.05.001.
  4. Stomper PC, Gelman RS. Mammography in symptomatic and asymptomatic patients. Hematol Oncol Clin North Am. 1989;3(4):611-40.
  5. Sharma D, Kumar R, Jain A. Breast cancer prediction based on neural networks and extra tree classifier using feature ensemble learning. Measurement: Sensors. 2022;24:100560. doi: 1016/j.measen.2022.100560.
  6. Mizzi D, Allely C, Zarb F, Kelly J, Hogg P, McEntee M, et al. Examining the effectiveness of supplementary imaging modalities for breast cancer screening in women with dense breasts: A systematic review and meta-analysis. Eur J Radiol. 2022;154:110416. doi: 10.1016/j.ejrad.2022.110416.
  7. Wilson JM, Colebaugh CA, Flowers KM, Overstreet D, Edwards RR, Maixner W, et al. Applying the Rapid OPPERA Algorithm to Predict Persistent pain outcomes among a cohort of women undergoing breast cancer surgery. J Pain. 2022;23(12):2003-12. doi: 10.1016/j.jpain.2022.07.012.
  8. Hessock M, Brewer T, Hutson S, Anderson J. Use of a standardized tool to identify women at risk for hereditary breast and ovarian. Nurs Womens Health. 2021;25(3):187-97. doi: 10.1016/j.nwh.2021.03.008. Erratum in: Nurs Womens Health. 2021;25(5):322.
  9. Shaikh FJ, Rao DS. Prediction of cancer disease using machine learning approach. Materials Today: Proceedings. 2022;50:40-7. doi:10.1016/ j.matpr.2021.03.625.
  10. Sawssen B, Okba T. A novel machine learning approach for breast cancer diagnosis. Measurement. 2022;187:110233. doi:10.1016/ j.measurement.2021. 110233.
  11. Gopal VN, Al-Turjman F, Kumar R, Anand L, Rajesh M. Feature selection and classification in breast cancer prediction using IoT and machine learning. Measurement. 2021;178:109442. doi:10.1016/j. measurement.2021.109442.
  12. Naji MA, Filali SE, Aarika K, Benlahmar EH, Abdelouhahid RA, Debauche O. Machine learning algorithms for breast cancer prediction and diagnosis. Procedia Computer Science. 2021;191:487-92. doi: 10.1016/j.procs.2021.07.062.
  13. Lu SY, Wang SH, Zhang YD. SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection. Comput Biol Med. 2022;148:105812. doi: 10.1016/j.compbiomed.2022.105812.
  14. Gao F, Wu T, Li J, Zheng B, Ruan L, Shang D, et al. SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis. Comput Med Imaging Graph. 2018;70:53-62. doi: 10.1016/j.compmedimag.2018.09.004.
  15. Shadab SA, Ansari MA, Singh N, Verma A, Tripathi P, Mehrotra R. Detection of cancer from histopathology medical image data using ML with CNN ResNet-50 architecture. In: Agrawal R, Ansari MA, Anand RS, et al., editors. Computational intelligence in healthcare applications. Academic Press; 2022.p.237-254. doi:10.1016/B978-0-323-99031-8.00007-7.
  16. Balkenende L, Teuwen J, Mann RM. Application of deep learning in breast cancer imaging. Semin Nucl Med. 2022;52(5):584-96. doi: 10.1053/j.semnuclmed.2022.02.003.
  17. Xue n, Zhou Q, Jiarong Ye, Rodney Long L, Antani S, Cornwell C, et al. Synthetic augmentation and feature-based filtering for improved cervical histopathology image classification.In: Shen, D, et al, editor. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science. Springer, Cham; 2019.p.387-396. doi: 10.1007/978-3-030-32239-7_43.
  18. Duggento A, Aiello M, Cavaliere C, Cascella GL, Cascella D, Conte G, 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;2019:5982834. doi: 10.1155/2019/5982834.
  19. Ragab DA, Sharkas M, Marshall S, Ren J. Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ. 2019;7:e6201. doi: 10.7717/peerj.6201.
  20. Fang Y, Zhao J, Hu L, Ying X, Pan Y, Wang, X. Image classification toward breast cancer using deeply-learned quality features. J Vis Commun Image Represent. 2019;64:102609. doi: 10.1016/j.jvcir.2019.102609.
  21. Deniz E, Şengür A, Kadiroğlu Z, Guo Y, Bajaj V, Budak Ü. Transfer learning based histopathologic image classification for breast cancer detection. Health Inf Sci Syst. 2018;6(1):18. doi: 10.1007/s13755-018-0057-x.
آمار
تعداد مشاهده مقاله: 3,261
تعداد دریافت فایل اصل مقاله: 745


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب