Deep Learning untuk Deteksi dan Segmentasi Kanker Payudara: A Systematic Literature Review menggunakan PRISMA

Nindiya Ika Nugraha Hartoyo; Agus Subhan Akbar; Alzena Dona Sabilla

doi:10.30736/jti.v11i1.1637

Authors

Nindiya Ika Nugraha Hartoyo Universitas Islam Nahdlatul Ulama Jepara https://orcid.org/0009-0001-2578-9701
Agus Subhan Akbar Universitas Islam Nahdlatul Ulama Jepara https://orcid.org/0000-0002-6011-7011
Alzena Dona Sabilla Universitas Islam Nahdlatul Ulama Jepara https://orcid.org/0009-0005-3048-5787

DOI:

https://doi.org/10.30736/jti.v11i1.1637

Keywords:

Deep Learning, Deteksi, Segmentasi, Kanker Payudara, Sistematik Literatur Review

Abstract

Kanker payudara adalah pertumbuhan sel abnormal yang menyebar dengan cepat ke sel lain dan berpotensi mencapai kegagalan suatu organ untuk berfungsi normal. Kehadirankan sel abnormal ini seringkali menandakan kondisi serius yang memerlukan perhatian medis. Deteksi dini sangat penting untuk pengobatan yang efektif dan mencegah perkembangan penyakit. Perkembangan teknologi AI, khususnya deep learning telah banyak diterapkan dalam analisis citra medis seperti mammografi, USG, dan MRI untuk deteksi serta segmentasi kanker payudara. Deep learning terbukti memiliki kapabilitas tinggi dalam mengenali pola serta memproses data citra medis secara efektif. Penelitian ini bertujuan menyusun tinjauan pustaka sistematis terkait implementasi deep learning untuk deteksi dan segmentasi kanker payudara. Dari total 196 artikel yang teridentifikasi, sebanyak 53 artikel terpilih setelah melalui proses seleksi menggunakan metode PRISMA dari database Scopus. Tinjauan ini meneliti model deep learning dan machine learning mulai dari pengaruh variasi dataset, metrik evaluasi, pengembangan arsitektur, serta penambahan blok khusus beserta pengaruhnya dalam model deep learning terhadap performa deteksi dan segmentasi kanker payudara. Namun, terdapat tantangan seperti bias dataset, skalabilitas di lingkungan dengan sumber daya terbatas, dan generalisasi. Tinjauan ini diharapkan dapat menunjukkan potensi deep learning dan machine learning untuk meningkatkan pengembangan model deteksi dan segmentasi di masa depan, berkontribusi pada peningkatan akurasi diagnosis, skalabilitas, serta hasil pengobatan pasien.

Downloads

Download data is not yet available.

References

[1] J. Renita, Kurniyati, and Y. Puspita, “The Effect Of The Combination Media Leaflet Lecture Method On The Knoewlege Of Women Of Reproductive Age About Breast Self-Examination In The Working Area Of Pasar Kepahiang Health Center In 2023,” Jm, vol. 11, no. 2, pp. 265–271, 2023.

[2] Y. Kiromitis, “Understanding the Differences Between Breast Ultrasound Mammogram and MRI – A Comprehensive Guide,” International Ultasound Services, 2024.

[3] Z. Zhu, Y. Sun, and B. Honarvar Shakibaei Asli, “Early Breast Cancer Detection Using Artificial Intelligence Techniques Based on Advanced Image Processing Tools,” Electron., vol. 13, no. 17, pp. 1–45, 2024, doi: 10.3390/electronics13173575.

[4] A. A. Abdul Halim et al., “Existing and Emerging Breast Cancer Detection Technologies and its Challenges: A Review,” Appl. Sci., vol. 11, no. 22, 2021, doi: 10.3390/app112210753.

[5] S. Choudhery and T. Anderson, “Tomosynthesis-Guided Breast and Axillary Localizations: Tips and Tricks,” Br. J. Radiol., vol. 93, no. 1114, p. 20200495, Oct. 2020, doi: 10.1259/bjr.20200495.

[6] A. A. Nafea, M. AL-Mahdawi, K. M. A. Alheeti, M. S. I. Alsumaidaie, and M. M. AL-Ani, “A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection,” Baghdad Sci. J., vol. 21, no. 10, 2024, doi: 10.21123/bsj.2024.9443.

[7] G. Valvano et al., “Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging,” J. Healthc. Eng., vol. 2019, 2019, doi: 10.1155/2019/9360941.

[8] H. Li, D. Chen, W. H. Nailon, M. E. Davies, and D. I. Laurenson, “Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography,” IEEE Trans. Med. Imaging, vol. 41, no. 1, pp. 3–13, 2022, doi: 10.1109/TMI.2021.3102622.

[9] R. Vankdothu, M. A. Hameed, and H. Fatima, “A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method,” Comput. Electr. Eng., vol. 101, p. 107960, 2022, doi: https://doi.org/10.1016/j.compeleceng.2022.107960.

[10] A. Khamparia et al., “Diagnosis of Breast Cancer Based on Modern Mammography using H ybrid Transfer Learning,” Multidimens. Syst. Signal Process., vol. 32, no. 2, pp. 747–765, 2021, doi: 10.1007/s11045-020-00756-7.

[11] W. Agustiono, “Smart Villages in Indonesia in the Light of the Literature Review,” 9th Int. Conf. ICT Smart Soc. Recover Together, Recover Stronger Smarter Smartization, Gov. Collab. ICISS 2022 - Proceeding, 2022, doi: 10.1109/ICISS55894.2022.9915061.

[12] S. Sharmin, T. Ahammad, M. A. Talukder, and P. Ghose, “A Hybrid Dependable Deep Feature Extraction and Ensemble-Based Machine Learning Approach for Breast Cancer Detection,” IEEE Access, vol. 11, no. August, pp. 87694–87708, 2023, doi: 10.1109/ACCESS.2023.3304628.

[13] P. E. Jebarani, N. Umadevi, H. Dang, and M. Pomplun, “A Novel Hybrid K-Means and GMM Machine Learning Model for Breast Cancer Detection,” IEEE Access, vol. 9, pp. 146153–146162, 2021, doi: 10.1109/ACCESS.2021.3123425.

[14] A. S. Vibith and M. C. Jobin Christ, “GBDTMO: As New Option for Early-Stage Breast Cancer Detection and Classification Using Machine Learning,” Automatika, vol. 64, no. 4, pp. 858–867, 2023, doi: 10.1080/00051144.2023.2226946.

[15] A. R. Vaka, B. Soni, and S. R. K., “Breast Cancer Detection by Leveraging Machine Learning,” ICT Express, vol. 6, no. 4, pp. 320–324, 2020, doi: 10.1016/j.icte.2020.04.009.

[16] G. Madhu et al., “UCapsNet: A Two-Stage Deep Learning Model Using U-Net and Capsule Network for Breast Cancer Segmentation and Classification in Ultrasound Imaging,” Cancers (Basel)., vol. 16, no. 22, 2024, doi: 10.3390/cancers16223777.

[17] L. Solorzano, S. Robertson, B. Acs, J. Hartman, and M. Rantalainen, “Ensemble-Based Deep Learning Improves Detection of Invasive Breast Cancer in Routine Histopathology Images,” Heliyon, vol. 10, no. 12, p. e32892, 2024, doi: 10.1016/j.heliyon.2024.e32892.

[18] R. Qasrawi et al., “Hybrid Ensemble Deep Learning Model for Advancing Breast Cancer Detection and Classification in Clinical Applications,” Heliyon, vol. 10, no. 19, p. e38374, 2024, doi: 10.1016/j.heliyon.2024.e38374.

[19] C. van Dooijeweert et al., “Clinical Implementation of Artificial Intelligence Assisted Detection of Breast Cancer Metastases in Sentinel Lymph Nodes: The CONFIDENT-B Single-Center, Non-Randomized clinical trial,” Nat. Cancer, vol. 5, no. 8, pp. 1195–1205, 2024, doi: 10.1038/s43018-024-00788-z.

[20] J. A. Retamero et al., “Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases,” Am. J. Surg. Pathol., vol. 48, no. 7, pp. 846–854, 2024, doi: 10.1097/PAS.0000000000002248.

[21] P. A. Bannier et al., “Development of A Deep-Learning Model Tailored for HER2 Detection In Breast Cancer to Aid Pathologists in Interpreting HER2-Low Cases,” Histopathology, vol. 85, no. 3, pp. 478–488, 2024, doi: 10.1111/his.15274.

[22] N. Harnischmacher, E. Rodner, and C. H. Schmitz, “Detection of Breast Cancer using Machine Learning on Time-Series Diffuse Optical Transillumination Data,” J. Biomed. Opt., vol. 29, no. 11, pp. 1–18, 2024, doi: 10.1117/1.jbo.29.11.115001.

[23] J. V. Johansson and E. Engström, “Radiologists’ Perceptions of Using Artificial Intelligence for Breast Cancer Detection in Mammography Screening in a Clinical Setting,” Health Informatics J., vol. 30, no. 3, 2024, doi: 10.1177/14604582241275020.

[24] F. D. Cortes-Rojas, Y. M. Hernández-Rodríguez, R. Bayareh-Mancilla, and O. E. Cigarroa-Mayorga, “An Artificial Intelligence-Based Tool for Enhancing Pectoral Muscle Segmentation in Mammograms: Addressing Class Imbalance and Validation Challenges in Automated Breast Cancer Diagnosis,” Diagnostics, vol. 14, no. 19, 2024, doi: 10.3390/diagnostics14192144.

[25] A. H. Elnaggar, A. S. A. El-Hameed, M. A. Yakout, and N. F. F. Areed, “Machine Learning for Breast Cancer Detection with Dual-Port Textile UWB MIMO Bra-Tenna System,” Inf., vol. 15, no. 8, 2024, doi: 10.3390/info15080467.

[26] V. Sureshkumar, R. S. N. Prasad, S. Balasubramaniam, D. Jagannathan, J. Daniel, and S. Dhanasekaran, “Breast Cancer Detection and Analytics Using Hybrid CNN and Extreme Learning Machine,” J. Pers. Med., vol. 14, no. 8, 2024, doi: 10.3390/jpm14080792.

[27] M. L. Taylor et al., “Single Vesicle Surface Protein Profiling and Machine Learning-Based Dual Image Analysis for Breast Cancer Detection,” Nanomaterials, vol. 14, no. 21, pp. 1–15, 2024, doi: 10.3390/nano14211739.

[28] B. N. R. Kumar, N. C. Gowda, B. J. Ambika, H. N. Veena, B. Ben Sujitha, and D. R. Ramani, “An Efficient Breast Cancer Detection Using Machine Learning Classification Models,” Int. J. Online Biomed. Eng., vol. 20, no. 13, pp. 24–40, 2024, doi: https://doi.org/10.3991/ijoe.v20i13.50289.

[29] A. Sahu, P. K. Das, and S. Meher, “Recent Advancements in Machine Learning and Deep Learning-Based Breast Cancer Detection Using Mammograms,” Phys. Medica, vol. 114, no. July, p. 103138, 2023, doi: 10.1016/j.ejmp.2023.103138.

[30] E. Tekin et al., “Tubule-U-Net: A Novel Dataset and Deep Learning-Based Tubule Segmentation Framework in Whole Slide Images of Breast Cancer,” Sci. Rep., vol. 13, no. 1, pp. 1–11, 2023, doi: 10.1038/s41598-022-27331-3.

[31] S. Pertuz et al., “Saliency Of Breast Lesions in Breast Cancer Detection Using Artificial Intelligence,” Sci. Rep., vol. 13, no. 1, pp. 1–9, 2023, doi: 10.1038/s41598-023-46921-3.

[32] L. A. Mullen, W. C. Walton, M. P. Williams, K. S. Peyton, and D. W. Porter, “Breast Cancer Detection with Upstream Data Fusion, Machine Learning, and Automated Registration: Initial Results,” J. Med. Imaging, vol. 10, no. S2, pp. 1–20, 2023, doi: 10.1117/1.jmi.10.s2.s22409.

[33] H. N. Huynh, A. T. Tran, and T. N. Tran, “Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms,” Appl. Sci., vol. 13, no. 12, 2023, doi: 10.3390/app13126894.

[34] R. Aljondi, S. S. Alghamdi, A. Tajaldeen, S. Alassiri, M. H. Alkinani, and T. Bertinotti, “Application of Artificial Intelligence in the Mammographic Detection of Breast Cancer in Saudi Arabian Women,” Appl. Sci., vol. 13, no. 21, 2023, doi: 10.3390/app132112087.