Efektivitas Penggunaan Seleksi Ciri CFS pada Klasifikasi Ciri Bentuk Nodul Kanker Payudara dengan Citra Ultrasonografi

Hesti Khuzaimah Nurul Yusufiyah*  -  (Scopus ID: 57189248053), Biomedical Engineering Group Research, Surabaya, Indonesia
Juan Pandu Gya Nur Rochman    -  Institut Teknologi Sepuluh Nopember, Indonesia

(*) Corresponding Author
The implementation of nodule shape characteristics is one of the parameters used in determining breast cancer malignancy. Mathematical calculations are used as a second decision to strengthen radiologists in determining breast cancer malignancy using ultrasound images (USG). The method used in this research is to filter ultrasound images that contain speckle noise, then continue the segmentation process, extracting shape features, selecting shape features, and classifying them. The feature selection process using Correlated based Feature Selection (CFS) is used to select the dominant shape features in the image. The classification results obtained show that the results of feature selection using CFS can improve the results of image accuracy, sensitivity and specificity, so as to be able to better distinguish the characteristic shape of the cancer nodule.

Keywords: Ultrasound; Breast cancer; CFS; Nodules

  1. Balocco, S., Gatta, C., Pujol, O., Mauri, J., & Radeva, P. 2010. SRBF: Speckle reducing bilateral filtering. Ultrasound in Medicine & Biology, 36(8), 1353–63. https://doi.org/10.1016/j.ultrasmedbio.2010.05.007
  2. Chen, C., Chou, Y., Han, K., Tiu, C., Chiou, H., & Chiou, S. 2003. Breast Lesions on Sonograms : Computer-aided Diagnosis with Nearly Setting- Independent Features and Artificial Neural Networks 1. Radiology, 226(7).
  3. Huang, Y.-L., Chen, D.-R., Jiang, Y.-R., Kuo, S.-J., Wu, H.-K., & Moon, W. K. 2008. Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound. Ultrasound Obstet Gynecol, 32(4), 565–572. https://doi.org/10.1002/uog.5205
  4. Husna, D. A., Nugroho, H. A., & Soesanti, I. 2015. Performance Analysis of Edge and Detail Preserved Speckle Noise Reduction Filters for Breast Ultrasound Images. In The 2nd International Conference on Information Technology, Computer, and electrical Engineering (pp. 79–83). Semarang:
  5. Departement of Computer Engineering, Diponegoro University.
  6. Kemenkes RI, I. 2015. Situasi penyakit kanker. Jakarta Selatan.
  7. Kuo, W., Chang, R., Moon, W. K., Lee, C. C., & Chen, D. 2002. Computer-aided Diagnosis of Breast Tumors with Different US Systems 1. Academic Radiology, 9(1), 793–799.
  8. Nugroho, A., & Nugroho, H. A. 2015. Active Contour Bilateral Filtering for Breast Lesions Segmentation on Ultrasound Images. In 2015 International Conference on Science inInformation Technology (ICSITech). Yogyakarta. https://doi.org/978-1-4799-8385-8
  9. Nugroho, H. A., Yusufiyah, H. K. N., Adji, T. B., & Nugroho, A. 2015. Zernike Moment Feature Extraction for Classifying Lesion’s Shape of Breast Ultrasound Image.
  10. WHO. 2018. Latest global cancer data : Cancer burden rises to 18 . 1 million new cases and 9 . 6 million cancer deaths in 2018. france. Retrieved from https://www.uicc.org/iarc-release-latest-world-cancer-statistics
  11. Wu, S., Zhu, Q., & D, Y. X. P. 2013. Evaluation of Various Speckle Reduction Filters on Medical Ultrasound Images. In 35th Annual International Conference of the IEEE EMBS (pp. 1148–1151). Osaka.
  12. Wu, W.-J., & Moon, W. K. 2008. Ultrasound breast tumor image computer-aided diagnosis with texture and morphological features. Academic Radiology, 15(7), 873–80. https://doi.org/10.1016/j.acra.2008.01.010
  13. Yang, H., Chang, C., Huang, S., Chou, Y., & Li, P. 2007. Breast Ultrasound Computer-Aided Diagnosis Using Both Acoustic and Image Features. IEEE Ultrasonics Symposium.
  14. Yusufiyah, H. K. N., Nugroho, H. A., Adji, T. B., & Nugroho, A. 2015. Feature Extraction for Classifying Lesion ’ s Shape of Breast Ultrasound Images. In The 2nd International Conference on Information Technology, Computer, and electrical Engineering (pp. 105–109). Departement of Computer Engineering, Diponegoro University.

Open Access Copyright (c) 2021 Physics Education Research Journal
Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

 
apps