150
Views
9
CrossRef citations to date
0
Altmetric
Original Research

An automated mammogram classification system using modified support vector machine

, , &
Pages 275-284 | Published online: 12 Aug 2019

References

  • Erhabor O, Abdulrahaman Y, Retsky M, et al. Breast Cancer in Nigeria: Diagnosis, Management and Challenges. UK: AuthorHouse; 2016.
  • Said MA, Henry AS, Ewunonu EO, et al. Breast cancer mortality in a resource-poor country: a 10-year experience in a tertiary institution. Sahel Med J. 2017;20:77–93.
  • Charate AP, Jamge SB. The preprocessing methods of mammogram images for breast cancer detection. IJRITCC. 2017;5(1):261–264.
  • Andreea GI, Pegza R, Lascu L, et al. The role of imaging techniques in diagnosis of breast cancer. Curr Health Sci J. 2011;37(2):241–248.
  • Duijm LE, Louwman MW, Groenewoud JH, et al. Inter-observer variability in mammography screening and effect of type and number of readers on screening outcome. Br J Cancer. 2009;100(6):901–907.19259088
  • Elmore JG, Miglioretti DL, Reisch LM, et al. Screening mammograms by community radiologists: variability in false-positive rates. J Natl Cancer Inst. 2002;94(18):1373–1380.12237283
  • Azar AT. Statistical analysis for radiologists’ interpretations variability in mammograms. Int J Syst Biol Biomed Technol. 2014;1(4):28–46. doi:10.4018/ijsbbt.2012100103
  • Dougherty G. Image analysis in medical imaging: recent advances in selected examples. Biomed Imaging Interv J. 2010;6(3):1–10.
  • Suckling J, Parker J, Dance DR, et al. The mammographic image analysis society digital mammogram database. In: Gale AG, Astley SM, Dance DR, Cairns AY (editors). Proceedings of the 2nd International Workshop on Digital Mammography Amsterdam: Excerpta Medica; 1994.
  • Luqman MM, Nor AMI. Preprocessing technique for mammographic images. Int J Comput Sci Inf Technol Res. 2014;2(4):226–231.
  • Kayode AA, Odeniyi OA, Efunboade AO. Enhancement and segmentation of mammograms for further analysis. IJCSIS. 2017;15(6):417–424.
  • Vasantha M, Bharathi VS, Dhamodharan R. Medical image feature extraction, selection and classification. Int J Eng Sci Technol. 2010;2(6):2071–2076.
  • Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;SMC-3(6):610–621.
  • Soh LK, Tsatsoulis C. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens. 1999;37(2):780–795.
  • Khuzi AM, Besar R, Zaki WM, et al. Identification of masses in digital mammogram using gray level co-occurrence matrices. Biomed Imaging Interv J. 2009;5(3):1–13.
  • Nithya R, Santhi B. Classification of normal and abnormal patterns in digital mammograms for the diagnosis of breast cancer. Int J Comput Appl. 2011;28(6):21–26.
  • Maitra IK, Nag S, Bandyopadhyay SB. Identification of abnormal masses in digital mammography images. Int J Comput Graphics. 2011;2(1):17–30.
  • Pradeep N, Girisha H, Sreepathi B, et al. Feature extraction of mammograms. Int J Bioinformatics Res. 2012;4(1):241–244.
  • Savita L., Rupali T, Almas S, Prapti DD.Detection and classification of breast mass using support vector machine. IOSR J Comput Eng. 2011;1–6.
  • Domínguez AR, Nandi AK. Toward breast cancer diagnosis based on automated segmentation of masses in mammograms. Pattern Recognit. 2009;42:1138–1148. doi:10.1016/j.patcog.2008.08.006
  • Rejani YIA, Selvi ST. Early detection of breast cancer using SVM classifier technique. Int J Comput Sci Eng. 2009;1(3):127–130.
  • Moayedi F, Azimifar Z, Boostani R, Katebi S. Contourlet-based mammography mass classification using the SVM family. Comput Biol Med. 2010;40(4):373–383. doi:10.1016/j.compbiomed.2009.12.00620181330
  • Dheeba J, Tamil S. Classification of malignant and benign microcalcification using SVM classifier. 2011 International Conference on Emerging Trends in Electrical and Computer Technology 2011:686–690.
  • Kavitha S, Thyagharajan KK. Features Based Mammogram Image Classification Using Weighted Feature Support Vector Machine. International Conference on Computing and Communication Systems. Berlin (Heidelberg): Springer; 2011:320–329.
  • Zhang E, Wang F, Li Y, Bai X. Automatic detection of microcalcifications using mathematical morphology and a support vector machine. Biomed Mater Eng. 2014;24(1):53–59.2. doi:10.3233/BME-130783
  • Kamra A, Jain VK, Singh S, Mittal S. Characterization of architectural distortion in mammograms based on texture analysis using support vector machine classifier with clinical evaluation. J Digit Imaging. 2016;29(1):104–114.26138756
  • Rouhi R, Jafari M, Kasaei S, Keshavarzian P. Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl. 2015;42(3):990–1002. doi:10.1016/j.eswa.2014.09.020.
  • Damasceno E, Oseas A, Filho DC, et al. Method of differentiation of benign and malignant masses in digital mammograms using texture analysis based on phylogenetic diversity. Comput Electr Eng. 2018:210–222. doi:10.1016/j.compeleceng.2018.03.038
  • Kaur P, Singh G, Kaur P. Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Inf Med Unlocked. 2019;1–19. doi:10.1016/j.imu.2019.01.001