(45-2) 09 * << * >> * Русский * English * Содержание * Все выпуски

Benign and malignant breast tumors classification based on texture analysis and backpropagation neural network
L.M. Wisudawati 1, S. Madenda 1, E.P. Wibowo 1, A.A. Abdullah 2

1 Faculty of Computer Science and Information Technology, University of Gunadarma,
Jl. Margonda Raya No.100, Pondok Cina, Kecamatan Beji, Kota Depok, Jawa Barat, 16424,

2 Faculty of Medicine, University of Gunadarma,
Jl. Margonda Raya No.100, Pondok Cina, Kecamatan Beji, Kota Depok, Jawa Barat, 16424

 PDF, 1267 kB

DOI: 10.18287/2412-6179-CO-769

Страницы: 227-234.

Язык статьи: English

Аннотация:
Breast cancer is a leading cause of death in women due to cancer. According to WHO, in 2018, it is estimated that 627.000 women died from breast cancer, that is approximately 15 % of all cancer deaths among women [3]. Early detection is a very important factor to reduce mortality by 25–30 %. Mammography is the most commonly used technique in detecting breast cancer using a low-dose X-ray system in the examination of breast tissue that can reduce false positives. A Computer-Aided Detection (CAD) system has been developed to effectively assist radiologists in detecting masses on mammograms that indicate the presence of breast tumors. The type of abnormality in mammogram images can be seen from the presence of microcalcifications and the presence of mass lesions. In this research, a new approach was developed to improve the performance of CAD System for classifying benign and malignant tumors. Areas suspected of being masses (RoI) in mammogram images were detected using an adaptive thresholding method and mathematical morphological operations. Wavelet decomposition is performed on the Region of Interest (RoI) and the feature extraction process is performed using a GLCM method with 4 statistical features, namely, contrast, correlation, entropy, and homogeneity. Classification of benign and malignant tumors using the MIAS database provided an accuracy of 95.83 % with a sensitivity of 95.23 % and a specificity of 96.49 %. A comparison with other methods illustrates that the proposed method provides better performance.

Ключевые слова:
mammography, CAD system, adaptive thresholding, mathematical morphology, wavelet, GLCM, artificial neural network.

Благодарности
The work was fully funded and supported by Gunadarma University, Indonesia.

Citation:
Wisudawati LM, Madenda S, Wibowo EP, Abdullah AA. Benign and malignant breast tumors classification based on texture analysis and backpropagation neural network. Computer Optics 2021; 45(2): 227-234. DOI: 10.18287/2412-6179-CO-769.

Литература:
  1. Gonzalez RC, Woods RE, Masters BR. Digital Image Processing. 3rd ed. J Biomed Opt 2009; 14(2): 029901. DOI: 10.1117/1.3115362.
  2. Gandhamal A, Talbar S, Gajre S, Hani AFM, Kumar D. Local gray level S-curve transformation: A generalized contrast enhancement technique for medical images. Comput Biol Med 2017, 83, 120-133. DOI: 10.1016/j.compbiomed.2017.03.001.
  3. World Health Organization (WHO). Early diagnosis and screening breast cancer. Geneva: World Health Organization; 2018. Source: <https://www.who.int/cancer/prevention/ diagnosis-screening/breast-cancer/en/>.
  4. Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL. A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data 2017; 4: 170177. DOI: 10.1038/sdata.2017.177.
  5. Madenda S. Image processing and digital video: Theory, application, and programming using Matlab. Jakarta: Erlangga Publisher; 2015.
  6. Mane SA, Kulhalli DKV. Mammogram image features extraction and classification for breast cancer detection. International Research Journal of Engineering and Technology (IRJET) 2015; 2(7): 810-814.
  7. Tortajada M, Oliver A, Martí R, Vilagran M, Ganau S, Tortajada L, Sentís M, Freixenet J. Adapting breast density classification from digitized to full-field digital mammograms. In Book: Maidment ADA, Bakic PR, Gavenonis S, eds. Breast imaging. Berlin, Heidelberg: Springer; 2012: 561-568. DOI: 10.1007/978-3-642-31271-7_72.
  8. Abdelsamea MM, Mohamed MH, Bamatraf M. Automated classification of malignant and benign breast cancer lesions using neural networks on digitized mammograms. Cancer Inform 2019; 18: 1-3. DOI: 10.1177/1176935119857570.
  9. Pawar MM, Talbar SN, Dudhane A. Local binary patterns descriptor based on sparse curvelet coefficients for false-positive reduction in mammograms. J Healthc Eng 2018; 2018: 5940436. DOI: 10.1155/2018/5940436.
  10. Pratiwi M, Alexander, Harefa J, Nanda S. Mammograms classification using gray-level co-occurrence matrix and radial basis function neural network. Procedia Comput Sci 2015; 59: 83-91. DOI: 10.1016/j.procs.2015.07.340.
  11. Salve SM. Mammographic image classification using gabor wavelet. International Research Journal of Engineering and Technology (IRJET) 2016; 03(03): 202-207.
  12. Anand S, Gayathri S. Mammogram image enhancement by two-stage adaptive histogram equalization. Optik –International Journal for Light and Electron Optics 2015; 126(21): 3150-3152. DOI: 10.1016/j.ijleo.2015.07.069.
  13. Tortajada M, Oliver A, Martí R, Ganau S, Tortajada L, Sentís M, Freixenet J, Zwiggelaar R. Breast peripheral area correction in digital mammograms. Comput Biol Med 2014; 50: 32-40. DOI: 10.1016/j.compbiomed.2014.03.010.
  14. Wisudawati LM, Madenda S, Wibowo EP, Abdullah AA. Feature extraction optimization with combination 2D-discrete wavelet transform and gray level co-occurrence matrix for classifying normal and abnormal breast tumors. Modern Applied Science 2020; 14(5): 51-62. DOI: 10.5539/mas.v14n5p51.
  15. Lladó X, Oliver A, Freixenet J, Martí R, Martí J. A textural approach for mass false positive reduction in mammography. Comput Med Imaging Graph 2009; 33(6): 415-422. DOI: 10.1016/j.compmedimag.2009.03.007.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: ko@smr.ru ; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20