Implementasi Metode Convolutional Neural Network untuk Klasifikasi Breast Cancer pada Citra Histopatologi
DOI:
https://doi.org/10.30865/mib.v7i1.5194Keywords:
Breast Cancer, CNN, Deep Learning, Loss Funtion, ReLuAbstract
Breast cancer is a tumor that manifests as an abnormal lump in the breast of a woman. The occurrence of breast cancer in women can be triggered by genetic and lifestyle factors. According to Global Cancer Statistics, 600,000 out of 2.3 million occurrences of breast cancer result in death. The death rate from breast cancer in Indonesia is likewise relatively high, reaching 17% for every 100,000 female inhabitants. Using histological pictures to diagnose breast cancer is one technique. The patient will capture an image of her breast cells, which will be examined and diagnosed by medical personnel. Even if histopathological scans are utilized as a baseline for the detection of breast cancer, the death rate associated with this disease remains rather high. One of the causes for the high mortality rate associated with breast cancer is the late detection of the disease, which results in patients being treated when the disease is in a severe state, and sometimes a misdiagnosis. The authors propose creating a breast cancer classification model utilizing the convolutional neural network (CNN) method in order to address the described issues. The study's findings indicate that CNN can classify breast cancer patients with an accuracy of 85 percent. Moreover, by calculating the loss function, the constructed model prevents overfitting.References
A. G. Waks and E. P. Winer, “Breast Cancer Treatment: A Review,†JAMA - Journal of the American Medical Association, vol. 321, no. 3, pp. 288–300, 2019, doi: 10.1001/jama.2018.19323.
Z. Momenimovahed and H. Salehiniya, “Epidemiological characteristics of and risk factors for breast cancer in the world,†Breast Cancer: Targets and Therapy, vol. 11, pp. 151–164, 2019, doi: 10.2147/BCTT.S176070.
H. Sung et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,†CA Cancer J Clin, vol. 71, no. 3, pp. 209–249, 2021, doi: 10.3322/caac.21660.
Kemkes, “Penyakit Kanker di Indonesia Berada Pada Urutan 8 di Asia Tenggara dan Urutan 23 di Asia,†2019. http://p2p.kemkes.go.id/penyakit-kanker-di-indonesia-berada-pada-urutan-8-di-asia-tenggara-dan-urutan-23-di-asia/
S. L. Anwar et al., “Pathological profiles and clinical management challenges of breast cancer emerging in young women in Indonesia: A hospital-based study,†BMC Womens Health, vol. 19, no. 1, pp. 1–8, 2019, doi: 10.1186/s12905-019-0724-3.
S. L. Anwar et al., “Risk factors of distant metastasis after surgery among different breast cancer subtypes: A hospital-based study in Indonesia,†World J Surg Oncol, vol. 18, no. 1, pp. 1–16, 2020, doi: 10.1186/s12957-020-01893-w.
R. Krithiga and P. Geetha, “Breast Cancer Detection, Segmentation and Classification on Histopathology Images Analysis: A Systematic Review,†Archives of Computational Methods in Engineering, vol. 28, no. 4, pp. 2607–2619, 2021, doi: 10.1007/s11831-020-09470-w.
E. Deniz, A. Şengür, Z. Kadiroğlu, Y. Guo, V. Bajaj, and Ü. Budak, “Transfer learning based histopathologic image classification for breast cancer detection,†Health Inf Sci Syst, vol. 6, no. 1, pp. 1–7, 2018, doi: 10.1007/s13755-018-0057-x.
M. Liu et al., “A Deep Learning Method for Breast Cancer Classification in the Pathology Images,†IEEE J Biomed Health Inform, 2022, doi: 10.1109/JBHI.2022.3187765.
Z. Gao, Z. Lu, J. Wang, S. Ying, and J. Shi, “A Convolutional Neural Network and Graph Convolutional Network Based Framework for Classification of Breast Histopathological Images,†IEEE J Biomed Health Inform, vol. 26, no. 7, pp. 3163–3173, Jul. 2022, doi: 10.1109/JBHI.2022.3153671.
A. Rodriguez-Ruiz et al., “Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists,†J Natl Cancer Inst, vol. 111, no. 9, pp. 916–922, 2019, doi: 10.1093/jnci/djy222.
Z. Zainudin, S. M. Shamsuddin, and S. Hasan, “Deep Layer CNN Architecture for Breast Cancer Histopathology Image Detection,†in The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019), 2019, pp. 43–51. doi: https://doi.org/10.1007/978-3-030-14118-9_5.
Z. Zainudin, S. M. Shamsuddin, and S. Hasan, “Deep Learning Layer Convolutional Neural Network (CNN) Scheme for Cancer Image,†IOP Conf Ser Mater Sci Eng, vol. 551, no. 1, 2019, doi: 10.1088/1757-899X/551/1/012039.
F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, “A Dataset for Breast Cancer Histopathological Image Classification,†IEEE Trans Biomed Eng, vol. 63, no. 7, pp. 1455–1462, Jul. 2016, doi: 10.1109/TBME.2015.2496264.
A. Mohammed Abd-Alsalam Selami and A. Freidoon Fadhil, “A Study of the Effects of Gaussian Noise on Image Features,†Kirkuk University Journal-Scientific Studies, vol. 11, no. 3, pp. 152–169, Sep. 2016, doi: 10.32894/kujss.2016.124648.
P. Oza, P. Sharma, S. Patel, and P. Kumar, “Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey,†Neural Computing and Applications, vol. 34, no. 3. Springer Science and Business Media Deutschland GmbH, pp. 1815–1836, Feb. 01, 2022. doi: 10.1007/s00521-021-06804-y.
F. Gao et al., “SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis,†Computerized Medical Imaging and Graphics, vol. 70, pp. 53–62, Dec. 2018, doi: 10.1016/j.compmedimag.2018.09.004.
S. Kaymak, A. Helwan, and D. Uzun, “Breast cancer image classification using artificial neural networks,†in Procedia Computer Science, 2017, vol. 120, pp. 126–131. doi: 10.1016/j.procs.2017.11.219.
B. Biswas, S. Kr. Ghosh, and A. Ghosh, “A Robust Multi-label Fruit Classification Based on Deep Convolution Neural Network,†2020, pp. 105–115. doi: 10.1007/978-981-13-9042-5_10.
M. Z. Alom et al., “A State-of-the-Art Survey on Deep Learning Theory and Architectures,†Electronics (Basel), vol. 8, no. 3, p. 292, Mar. 2019, doi: 10.3390/electronics8030292.
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