Enhanced Breast Cancer Diagnosis Using a Novel Hybrid Deep Learning Approach

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 12 | Issue : 02 | Page : –
    By

    Balakrishna Gurmitkal,

  • Ismail B,

  1. Research Scholar, Department of Statistics, Yenepoya (Deemed to be University), Deralakatte, Mangalore, Karnataka, India
  2. Professor & HOD, Department of Statistics, Yenepoya (Deemed to be University), Deralakatte, Mangalore, Karnataka, India

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_191247’);});Edit Abstract & Keyword

Breast cancer is a disease characterized by the uncontrolled growth and multiplication of abnormal cells in the breast, leading to the formation of tumors. Breast cancer can spread to nearby lymph nodes or other organs in the body, which can be life-threatening. Risk factors for breast cancer include increasing age, obesity, alcohol use, family history, and reproductive history. However, many women diagnosed with breast cancer do not have known risk factors. Deep learning (DL) techniques have effectivelyaddressed this challenge for complex prediction tasks. Our research introduces a novel approach,Swarm Optimized Convo Neuro recurrence net (SO-CNRN), based on profound learning principles, to predict breast cancer. We collected a dataset from an online source. Then, a step pre-processing methodology based on Z-score normalization will be implemented to extract the mean and standard values in the data. To extract meaningful features, we employ principal component analysis (PCA), which aids in reducing dimensionality while retaining essential features.Next, the features are selected using a Lasso regression, which determines the particular features. Finally, the classification task is accomplished using the proposed model, which inherently considers temporal dependencies in the data. This is crucial to detect breast cancer, where historical patterns significantly influence outcomes. The proposed research uses Python tools, focusing on breast cancer detection.The efficiency of the proposed model is assessed through accuracy, precision, recall, F1-score and AUC. Compared to existing methods, our comprehensive approach aims to enhance breast cancer detection with an accuracy of 97.03%, precision of 97%, recall value of 97%, F1-score of 97% and AUC of 0.99.

Keywords: Breast cancer, python, Swarm Optimized Convo Neuro recurrence net (SO-CNRN), Z-score normalization, Principal component analysis (PCA), Lasso regression, Deep learning.

[This article belongs to Research & Reviews: A Journal of Bioinformatics ]

How to cite this article:
Balakrishna Gurmitkal, Ismail B. Enhanced Breast Cancer Diagnosis Using a Novel Hybrid Deep Learning Approach. Research & Reviews: A Journal of Bioinformatics. 2025; 12(02):-.
How to cite this URL:
Balakrishna Gurmitkal, Ismail B. Enhanced Breast Cancer Diagnosis Using a Novel Hybrid Deep Learning Approach. Research & Reviews: A Journal of Bioinformatics. 2025; 12(02):-. Available from: https://journals.stmjournals.com/rrjobi/article=2025/view=0


document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_191247’);});Edit

References

  1. Cleveland Clinic. Breast cancer [Internet]. 2024 [cited 2024 Mar 24]. Available from: https://my.clevelandclinic.org/health/diseases/3986-breast-cancer
  2. American Society of Clinical Oncology. Breast cancer: Introduction [Internet]. 2024 [cited 2024 Mar 24]. Available from: https://www.cancer.net/cancer-types/breast-cancer/introduction
  3. National Cancer Institute. Breast cancer [Internet]. 2024 [cited 2024 Mar 24]. Available from: https://www.cancer.gov/types/breast?redirect=true
  4. Thorat MA, Balasubramanian R. Breast cancer prevention in high-risk women. Best Pract Res Clin Obstet Gynaecol. 2020;65:18-31. doi:10.1016/j.bpobgyn.2019.11.006
  5. Kumar M, Singhal S, Shekhar S, Sharma B, Srivastava G. Optimized stacking ensemble learning model for breast cancer detection and classification using machine learning. Sustainability. 2022;14(21):13998. doi:10.3390/su142113998
  6. El-Sheikh N, Mousa NO, Tawfeik AM, et al. Assessment of human papillomavirus infection and risk factors in Egyptian women with breast cancer. Breast Cancer (Auckl). 2021;15:1178223421996279. doi:10.1177/1178223421996279
  7. Cathcart-Rake EJ, Ruddy KJ, Bleyer A, Johnson RH. Breast cancer in adolescent and young adult women under the age of 40 years. JCO Oncol Pract. 2021;17(6):305-313. doi:10.1200/OP.20.00793
  8. Cavdar E. Understanding breast cancer from the patients’ perspective. EJMI. 2021;5(1):1-7. doi:10.14744/ejmi.2021.70558
  9. Zewdie ET, Tessema AW, Simegn GL. Classification of breast cancer types, sub-types and grade from histopathological images using deep learning technique. Health Technol. 2021;11(6):1277-1290. doi:10.1007/s12553-021-00592-0
  10. Saini A, Kumar M, Bhatt S, Saini V, Malik A. Cancer causes and treatments. Int J Pharm Sci Res. 2020;11(7):3121-3134. doi:10.13040/IJPSR.0975-8232.11(7).3109-22
  11. Rabiei R. Prediction of breast cancer using machine learning approaches. J Biomed Phys Eng. 2022;12(3):1-10. doi:10.31661/jbpe.v0i0.2109-1403
  12. Hamed G, Marey MAER, Amin SES, Tolba MF. Deep learning in breast cancer detection and classification. In: Hassanien AE, Azar AT, Gaber T, Oliva D, Tolba FM, editors. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). Vol 1153. Advances in Intelligent Systems and Computing. Springer International Publishing; 2020. p. 322-333. doi:10.1007/978-3-030-44289-7_30
  13. Jafarzadeh Ghoushchi S, Ranjbarzadeh R, Najafabadi SA, Osgooei E, Tirkolaee EB. An extended approach to the diagnosis of tumour location in breast cancer using deep learning. J Ambient Intell Human Comput. 2023;14(7):8487-8497. doi:10.1007/s12652-021-03613-y
  14. Kabiraj S, Raihan M, Alvi N, et al. Breast cancer risk prediction using XGBoost and random forest algorithm. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE; 2020. p. 1-4. doi:10.1109/ICCCNT49239.2020.9225451
  15. Wang H, Li Y, Khan SA, Luo Y. Prediction of breast cancer distant recurrence using natural language processing and knowledge-guided convolutional neural network. ArtifIntell Med. 2020;110:101977. doi:10.1016/j.artmed.2020.101977
  16. Resmini R, Silva L, Araujo AS, Medeiros P, Muchaluat-Saade D, Conci A. Combining genetic algorithms and SVM for breast cancer diagnosis using infrared thermography. Sensors. 2021;21(14):4802. doi:10.3390/s21144802
  17. Al-Haija QA, Adebanjo A. Breast cancer diagnosis in histopathological images using ResNet-50 convolutional neural network. In: 2020 IEEE International IoT, Electronics and Mechatronics Conference (IEMTRONICS). IEEE; 2020. p. 1-7. doi:10.1109/IEMTRONICS51293.2020.9216455
  18. Kaggle. Breast cancer dataset [Internet]. 2024 [cited 2024 Mar 24]. Available from: https://www.kaggle.com/datasets/yasserh/breast-cancer-dataset
  19. Wang X, Ahmad I, Javeed D, et al. Intelligent hybrid deep learning model for breast cancer detection. Electronics. 2022;11(17):2767. doi:10.3390/electronics11172767
  20. Shahidi F, Daud SM, Abas H, Ahmad NA, Maarop N. Breast cancer classification using deep learning approaches and histopathology image: A comparison study. IEEE Access. 2020;8:187531-52. doi:10.1109/ACCESS.2020.3030395
  21. Amrane M, Oukid S, Gagaoua I, Ensari T. Breast cancer classification using machine learning. In: 2018 Electric Electronics, Computer Science, Biomedical Engineering’s Meeting (EBBT). IEEE; 2018. p. 1-4. doi:10.1109/EBBT.2018.8397450
  22. Sarkar S, Mali K. Breast cancer subtypes classification with hybrid machine learning model. Methods Inf Med. 2022;61(03/04):68-83. doi:10.1055/s-0042-1742634
  23. Rathi M, Pareek V. Hybrid approach to predict breast cancer using machine learning techniques. Int J Comput Sci Eng. 2016;5(3):125-36.
  24. Wang X, Ahmad I, Javeed D, et al. Intelligent hybrid deep learning model for breast cancer detection. Electronics. 2022;11(17):2767. doi:10.3390/electronics11172767
  25. Dewangan KK, Dewangan DK, Sahu SP, Janghel R. Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique. Multimed Tools Appl. 2022;81(10):13935-60. doi:10.1007/s11042-021-11266-4
  26. Sahu B, Mohanty S, Rout S. A hybrid approach for breast cancer classification and diagnosis. EAI Endorsed Trans Scalable Inf Syst. 2019;6(20):e3. doi:10.4108/eai.9-4-2019.162356
  27. Dalal S, Onyema EM, Kumar P, et al. A hybrid machine learning model for timely prediction of breast cancer. Int J Model Simul Sci Comput. 2023;14(04):2341023. doi:10.1142/S1793962323410236
  28. Jebarani PE, Umadevi N, Dang H, Pomplun M. A novel hybrid K-means and GMM machine learning model for breast cancer detection. IEEE Access. 2021;9:146153-62. doi:10.1109/ACCESS.2021.3124426
  29. Omondiagbe DA, Veeramani S, Sidhu AS. Machine learning classification techniques for breast cancer diagnosis. IOP Conf Ser Mater Sci Eng. 2019;495:012033. doi:10.1088/1757-899X/495/1/012033
Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 09/01/2025
Accepted 02/04/2025
Published 31/05/2025
Publication Time 142 Days
153

[first_name] [last_name]

My IP

PlumX Metrics