Deep learning untuk pendeteksian penyakit kanker payudara dengan optimasi Adam

Irmawati Irmawati; Yuris Alkhalifi; Agung Fazriansyah; Mohammad Syamsul Azis; Kudiantoro Widianto

doi:10.52362/jisamar.v7i1.1015

Irmawati Irmawati Universitas Bina Sarana Informatika
Yuris Alkhalifi Universitas Bina Sarana Informatika
Agung Fazriansyah Universitas Bina Sarana Informatika
Mohammad Syamsul Azis Universitas Nusa Mandiri
Kudiantoro Widianto Universitas Bina Sarana Informatika

DOI: https://doi.org/10.52362/jisamar.v7i1.1015

Abstract

Breast cancer is the second leading cause of death in female patients in the world. Breast cancer has caused death in more than 100 countries. Early diagnosis of breast cancer patients is important to reduce the possibility of death. Researchers focus on accurate breast cancer detection, automated diagnostic methods and breast cancer diagnosis. This paper proposes Adam's optimization for Deep Learning Algorithm to classify breast cancer detection. This study aims to overcome the problem of data instability and overfitting, as well as update network weights on deep learning training data. In this study, the authors conducted experiments with a combination of three hidden layers and learning speed to improve classification accuracy. The experiment used the breast cancer data set obtained from the UCI Study: the WBCD data set (Original) while the experimental results showed that the proposed scheme achieved 96.3% accuracy for classifying breast cancer.

Downloads

Download data is not yet available.

References

[1] H. B. Lee And W. Han, “Unique Features Of Young Age Breast Cancer And Its Management,” J. Breast Cancer, Vol. 17, No. 4, Pp. 301–307, 2014.
[2] V. Chaurasia And S. Pal, “Applications Of Machine Learning Techniques To Predict Diagnostic Breast Cancer,” Sn Comput. Sci., Vol. 1, No. 5, Pp. 1–11, 2020.
[3] R. A. Khan, T. Suleman, M. S. Farooq, M. H. Rafiq, And M. A. Tariq, “Data Mining Algorithms For Classification Of Diagnostic Cancer Using Genetic Optimization Algorithms,” Ijcsns Int. J. Comput. Sci. Netw. Secur., Vol. 12, No. March, Pp. 207–211, 2017.
[4] E. Alickovic And A. Subasi, Normalized Neural Networks For Breast Cancer Classification, Vol. 73. Springer International Publishing, 2020.
[5] W. Majeed Et Al., “Breast Cancer: Major Risk Factors And Recent Developments In Treatment,” Asian Pacific J. Cancer Prev., Vol. 15, No. 8, Pp. 3353–3358, 2014.
[6] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, And A. Jemal, “Global Cancer Statistics 2018: Globocan Estimates Of Incidence And Mortality Worldwide For 36 Cancers In 185 Countries,” Ca. Cancer J. Clin., Vol. 68, No. 6, Pp. 394–424, 2018.
[7] A. E. Minarno And E. A. Hakim, “Klasifikasi Citra Thorax X - Ray Menggunakan Transfer Learning Inception V3,” Pp. 53–62, 2021.
[8] N. F. Idris And M. A. Ismail, “Breast Cancer Disease Classification Using Fuzzy-Id3 Algorithm With Fuzzydbd Method: Automatic Fuzzy Database Definition,” Peerj Comput. Sci., Vol. 7, Pp. 1–22, 2021.
[9] M. Pyingkodi, M. M., Shanthi, S., Saravanan, T. M., Thenmozhi, K., Nanthini, K., Hemalatha, D., ... & Dhivya, “Performance Study Of Classification Algorithms Using The Breast Cancer Dataset,” Int. J. Futur. Gener. Commun. Netw., 2020.
[10] H. Saoud, A. Ghadi, M. Ghailani, And B. A. Abdelhakim, Using Feature Selection Techniques To Improve The Accuracy Of Breast Cancer Classification, Vol. 1. Springer International Publishing, 2019.