Subclass based parallel learning neural network for classification of masses in mammograms

Abstract

Computer aided detection assists radiologists by providing second opinion in the mammography detection, and reduce misdiagnosis. An expert system with novel subclass based learning multiple neural network classifier (SBLMNN) has been proposed to solve the mammogram mass classification problem. This work explores the significance of the modular learning in artificial neural networks, inspired from the visual cortex basis of human learning. It is a two stage learning process. In stage I, the proposed architecture processes parallel on the radiological characteristics of mass like shape, margin and texture features in separate modules similar to the visual cortex to identify the subclasses. The intermediate outputs of the independent modules are processed to classify the mass into benign or malignant in stage II. Modularization and deep learning considered in the proposed method improves the performance of the classifier and speed of learning. For the experimental analysis, images are obtained from the mammogram image analysis society. The experiments were implemented in MATLAB. For benign and malignant classification, the shows that SBLMNN accuracy is 92%, which is higher than monolithic MLP neural network architecture.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. 1.

    Halls S (2016) Radiologist role in breast cancer diagnosis—Moose and doc. www.breast-cancer.ca/rdiolgst

  2. 2.

    Saki F, Tahmasbi A, Soltanian-Zadeh H, Shokouhi SB (2013) Fast opposite weight learning rules with application in breast cancer diagnosis. Comput Biol Med 43(1):32–41

    Article  Google Scholar 

  3. 3.

    Kharya S (2012) Using data mining techniques for diagnosis and prognosis of cancer disease. Int J Comput Sci Eng Inf Technol (IJCSEIT) 2(2):55–66

    Google Scholar 

  4. 4.

    Aarthi R, Divya, K, Kavitha S (2011) Application of feature extraction and clustering in mammogram classification using support vector machine. In: Third international conference on advanced computing (ICoAC), pp 62–67

  5. 5.

    Liu X, Tang J (2014) Mass classification in mammograms using selected geometry and texture features and a new SVM-based feature selection method. IEEE Syst J 8:910–920

    Article  Google Scholar 

  6. 6.

    Vadivel A, Surendiran B (2013) A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories. Comput Biol Med 43(4):259–67

    Article  Google Scholar 

  7. 7.

    Holalu S (2006) Breast tissue classification using statistical feature extraction of mammograms. Med Imag Inf Sci 23(3):105–107

    Google Scholar 

  8. 8.

    Oliver A (2010) A review of automatic mass detection and segmentation in mammographic images. Med Image Anal 14(2):87–110

    Article  Google Scholar 

  9. 9.

    Sahiner BS (1998) Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis. Med Phys 25(4):516–526

    Article  Google Scholar 

  10. 10.

    Keles A, Keles A, Yavuz U (2011) Expert system based on neuro-fuzzy rules for diagnosis breast cancer. Experts Syst Appl 38(5):5719–5726

    Article  Google Scholar 

  11. 11.

    Miranda GHB (2015) Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Comput Biol Med 64(1):334–346

    Article  Google Scholar 

  12. 12.

    Al Mutaz MA, Deris S, Zaki N, Ghoneim DM (2007) Breast cancer detection based on statistical textural features classification. In: Innovations in information technology. IIT ’07. 4th international conference on, Dubai, United Arab Emirates, 2007, pp 728–730. https://doi.org/10.1109/IIT.2007.4430510

  13. 13.

    Rangaraj M, Rangayyan M (2007) A review of computer-aided diagnosis of breast cancer: toward the detection of subtle signs. J Frankl Inst 344(3–4):312–348

    MathSciNet  MATH  Google Scholar 

  14. 14.

    Dinakaran K, Sivakrithika V (2013) A study on mammography computer aided diagnosis system using machine learning methods. In: Sustainable energy and intelligent systems (SEISCON 2013), IET fourth international conference on Chennai, pp 333–341. https://doi.org/10.1049/ic.2013.0334

  15. 15.

    Valarmathi P, Sivakrithika V, Dinakaran K (2016) Classification of mammogram masses using selected texture, shape and margin features with multilayer perceptron classifier. Biomed Res Int J Med Sci (special issue S310–S313). ISSN: 0970-938X

  16. 16.

    Jona JB (2012) A hybrid swarm optimization approach for feature set reduction in digital mammograms. WSEAS Trans Inf Sci Appl 9(11)

  17. 17.

    Auda G, Kamel M (1999) Modular neural networks: a survey. Int J Neural Syst 9(2):129–151

    Article  Google Scholar 

  18. 18.

    Pratiwi M, Alexander, Harefa J, Nanda S (2015) Mammograms classification using gray-level co-occurrence matrix and radial basis function neural network. In: International conference on computer science and computational intelligence (ICCSCI 2015), procedia computer science, vol 59, pp 83 – 91

  19. 19.

    Setiawan AS, Elysia, Wesley J, Purnama Y (2015) Mammogram classification using law’s texture energy measure and neural networks. In: International conference on computer science and computational intelligence (ICCSCI 2015), procedia computer science, vol 59, pp 92–97

  20. 20.

    Velmurugan M, Thangavel K, Boss RSC (2013) Mammogram classification using fuzzy neural network. Int J Comput Intell Inf 3(3). ISSN: 2349-6363 195

  21. 21.

    Verma B, McLeod P, Klevansky A (2010) Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer. Expert Syst Appl 37:3344–3351

    Article  Google Scholar 

  22. 22.

    Tahmasbi A, Saki F, Shokouhi SB (2011) Classification of benign and malignant masses based on Zernike moments. Comput Biol Med 41(8):726–735

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to V. Sivakrithika.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sivakrithika, V., Dinakaran, K. Subclass based parallel learning neural network for classification of masses in mammograms. Des Autom Embed Syst 22, 65–79 (2018). https://doi.org/10.1007/s10617-017-9198-4

Download citation

Keywords

  • Multiple neural networks
  • Mammograms
  • Computer aided diagnosis
  • Shape feature
  • Margin feature
  • Texture feature
  • Classification