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Detection of breast cancer using the infinite feature selection with genetic algorithm and deep neural network

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Abstract

The breast cancer is a major health issue worldwide, so the early detection of abnormalities decreases the mortality rate. For the early detection of breast cancer, a new model is proposed in this research using mammogram images which is an effective technique used for screening and detecting the breast cancer. At first, the images are acquired from the digital database for screening mammography and mammographic image analysis society datasets. Then, the visual quality of the images is improved by using normalization, contrast limited adaptive histogram equalization and median filter. Further, multi-level multi objective electro magnetism like optimization algorithm is proposed to segment the non-cancer and cancer regions from the enhanced images. Additionally, feature vectors are extracted from the segmented regions using local directional ternary pattern, histogram of oriented gradients and Haralick texture features. Next, infinite feature selection with genetic algorithm is applied to select the active or relevant features for breast cancer classification. The genetic algorithm is applied with entropy value to measure the homogeneity to find the mutual information between the extracted features that helps in provide prominent feature values. The genetic algorithm has the advantage of probabilistic transition rule that helps to analysis the associate neighbour features. The selected feature vectors are fed to deep neural network to classify the mammogram images as malignant and benign classes. From the simulation result, the proposed infinite feature selection with genetic algorithm and deep neural network showed 0. 10–7% improvement in accuracy related to the existing models such as an extreme learning machine, conditional generative adversarial network with convolutional neural network, etc.

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References

  1. Beura, S., Majhi, B., Dash, R.: Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing 154, 1–14 (2015)

    Article  Google Scholar 

  2. Pawar, M.M., Talbar, S.N.: Genetic fuzzy system (GFS) based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis. Perspect. Sci. 8, 247–250 (2016)

    Article  Google Scholar 

  3. Verma, B., McLeod, P., Klevansky, A.: Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer. Expert Syst. Appl. 37(4), 3344–3351 (2010)

    Article  Google Scholar 

  4. Shastri, A.A., Tamrakar, D., Ahuja, K.: Density-wise two stage mammogram classification using texture exploiting descriptors. Expert Syst. Appl. 99, 71–82 (2018)

    Article  Google Scholar 

  5. Agnes, S.A., Anitha, J., Pandian, S.I.A., Peter, J.D.: Classification of mammogram images using multiscale all convolutional neural network (MA-CNN). J. Med. Syst. 44(1), 30 (2020)

    Article  Google Scholar 

  6. Wang, X., Liang, G., Zhang, Y., Blanton, H., Bessinger, Z., Jacobs, N.: Inconsistent performance of deep learning models on mammogram classification. J. Am. Coll. Radiol. (2020). https://doi.org/10.1016/j.jacr.2020.01.006

    Article  Google Scholar 

  7. Arevalo, J., González, F.A., Ramos-Pollán, R., Oliveira, J.L., Lopez, M.A.G.: Representation learning for mammography mass lesion classification with convolutional neural networks. Comput. Methods Prog. Biomed. 127, 248–257 (2016)

    Article  Google Scholar 

  8. Chougrad, H., Zouaki, H., Alheyane, O.: Multi-label transfer learning for the early diagnosis of breast cancer. Neurocomputing (2019). https://doi.org/10.1016/j.neucom.2019.01.112

    Article  Google Scholar 

  9. Patil, R.S., Biradar, N.: Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network. Evolut. Intell. (2020). https://doi.org/10.1007/s12065-020-00403-x

    Article  Google Scholar 

  10. Mohanty, F., Rup, S., Dash, B., Majhi, B., Swamy, M.N.S.: Digital mammogram classification using 2D-BDWT and GLCM features with FOA-based feature selection approach. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04186-w

    Article  Google Scholar 

  11. Mohanty, F., Rup, S., Dash, B., Majhi, B., Swamy, M.N.S.: Mammogram classification using contourlet features with forest optimization-based feature selection approach. Multimed. Tools Appl. 78(10), 12805–12834 (2019)

    Article  Google Scholar 

  12. Nirmala, G., Suresh Kumar, P.: A novel bat optimized runlength networks (BORN) for an efficient classification of breast cancer. J. Ambient Intell. Humaniz. Comput. (2020). https://doi.org/10.1007/s12652-020-01890-7

    Article  Google Scholar 

  13. Vijayarajeswari, R., Parthasarathy, P., Vivekanandan, S., Basha, A.A.: Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement 146, 800–805 (2019)

    Article  Google Scholar 

  14. Singh, V.K., Rashwan, H.A., Romani, S., Akram, F., Pandey, N., Sarker, M.M.K., Saleh, A., Arenas, M., Arquez, M., Puig, D., Torrents-Barrena, J.: Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Syst. Appl. 139, 112855 (2020)

    Article  Google Scholar 

  15. Li, H., Zhuang, S., Li, D.A., Zhao, J., Ma, Y.: Benign and malignant classification of mammogram images based on deep learning. Biomed. Signal Process. Control 51, 347–354 (2019)

    Article  Google Scholar 

  16. Mohanty, F., Rup, S., Dash, B., Majhi, B., Swamy, M.N.S.: An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine. Appl. Soft Comput. 91, 106266 (2020)

    Article  Google Scholar 

  17. Sadad, T., Munir, A., Saba, T., Hussain, A.: Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature. J. Comput. Sci. 29, 34–45 (2018)

    Article  MathSciNet  Google Scholar 

  18. Ting, F.F., Tan, Y.J., Sim, K.S.: Convolutional neural network improvement for breast cancer classification. Expert Syst. Appl. 120, 103–115 (2019)

    Article  Google Scholar 

  19. Kaur, P., Singh, G., Kaur, P.: Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Inform. Med. Unlocked 16, 100151 (2019)

    Article  Google Scholar 

  20. Xie, W., Li, Y., Ma, Y.: Breast mass classification in digital mammography based on extreme learning machine. Neurocomputing 173, 930–941 (2016)

    Article  Google Scholar 

  21. Ahmed, L., Iqbal, M.M., Aldabbas, H., Khalid, S., Saleem, Y., Saeed, S.: Images data practices for semantic segmentation of breast cancer using deep neural network. J. Ambient Intell. Humaniz. Comput. (2020). https://doi.org/10.1007/s12652-020-01680-1

    Article  Google Scholar 

  22. Shayma’a, A.H., Sayed, M.S., Abdalla, M.I., Rashwan, M.A.: Breast cancer masses classification using deep convolutional neural networks and transfer learning. Multimed. Tools Appl. 79(41), 30735–30768 (2020)

    Google Scholar 

  23. Khan, H.N., Shahid, A.R., Raza, B., Dar, A.H., Alquhayz, H.: Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access 7, 165724–165733 (2019)

    Article  Google Scholar 

  24. Shen, Y., Wu, N., Phang, J., Park, J., Liu, K., Tyagi, S., Heacock, L., Kim, S.G., Moy, L., Cho, K., Geras, K.J.: An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization. Med. Image Anal. 68, 101908 (2021)

    Article  Google Scholar 

  25. Suganthi, M., Madheswaran, M.: An improved medical decision support system to identify the breast cancer using mammogram. J. Med. Syst. 36(1), 79–91 (2012)

    Article  Google Scholar 

  26. de Oliveira Martins, L., Silva, A.C., De Paiva, A.C., Gattass, M.: Detection of breast masses in mammogram images using growing neural gas algorithm and Ripley’s K function. J. Signal Process. Syst. 55(1–3), 77–90 (2009)

    Article  Google Scholar 

  27. Melloul, M., Joskowicz, L.: Segmentation of microcalcification in X-ray mammograms using entropy thresholding. In: CARS 2002 computer assisted radiology and surgery, pp. 671–676. Springer, Berlin (2002)

    Chapter  Google Scholar 

  28. Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, P., Moore, R., Chang, K., Munishkumaran, S.: Current status of the digital database for screening mammography. In: Digital mammography, pp. 457–460. Springer, Dordrecht (1998)

    Chapter  Google Scholar 

  29. Koo, K.M., Cha, E.Y.: Image recognition performance enhancements using image normalization. HCIS 7(1), 1–11 (2017)

    Google Scholar 

  30. Kuo, Y.L., Tai, C.W.: A simple and efficient median filter for removing high-density impulse noise in images. Int. J. Fuzzy Syst. 17(1), 67–75 (2015)

    Article  Google Scholar 

  31. Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J. VLSI Signal Process. Syst. Signal Image Video Technol. 38(1), 35–44 (2004)

    Article  Google Scholar 

  32. Qi, X., Zhang, L., Chen, Y., Pi, Y., Chen, Y., Lv, Q., Yi, Z.: Automated diagnosis of breast ultrasonography images using deep neural networks. Med. Image Anal. 52, 185–198 (2019)

    Article  Google Scholar 

  33. Cuevas, E., Zaldívar, D., Perez-Cisneros, M.: Otsu and Kapur segmentation based on harmony search optimization. In: Applications of evolutionary computation in image processing and pattern recognition, pp. 169–202. Springer, Cham (2016)

    Chapter  MATH  Google Scholar 

  34. Pruthi, J., Gupta, G.: Image segmentation using genetic algorithm and OTSU. In: Proceedings of fifth international conference on soft computing for problem solving, pp. 473–480. Springer, Singapore (2016)

    Chapter  Google Scholar 

  35. Avuti, S.K., Bajaj, V., Kumar, A., Singh, G.K.: A novel pectoral muscle segmentation from scanned mammograms using EMO algorithm. Biomed. Eng. Lett. 9(4), 481–496 (2019)

    Article  Google Scholar 

  36. Anusha, R., Jaidhar, C.D.: Human gait recognition based on histogram of oriented gradients and Haralick texture descriptor. Multimed. Tools Appl. (2020). https://doi.org/10.1007/s11042-019-08469-1

    Article  Google Scholar 

  37. Chahi, A., Ruichek, Y., Touahni, R.: Local directional ternary pattern: a new texture descriptor for texture classification. Comput. Vis. Image Underst. 169, 14–27 (2018)

    Article  Google Scholar 

  38. Rangappa, V.G., Prasad, S.V.A.V., Agarwal, A.: Infinite feature selection based arrhythmia classification using semantic features and random forest. Int. J. Intell. Eng. Syst. 12(3), 203–213 (2019)

    Google Scholar 

  39. Nguyen, T.G., Phan, T.V., Hoang, D.T., Nguyen, T.N., So-In, C.: Efficient SDN-based traffic monitoring in iot networks with double deep Q-network. In: International conference on computational data and social networks, pp. 26–38. Springer, Cham (2020)

    Chapter  Google Scholar 

  40. Birbil, Şİ, Fang, S.C.: An electromagnetism-like mechanism for global optimization. J. Glob. Optim. 25(3), 263–282 (2003)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Hirasugar Institute of Technology, Nidasoshi, India

    S. S. Ittannavar

  2. Department of Biomedical Engineering, KLE Dr. MS Sheshgiri College of Engineering and Technology, Belagavi, India

    R. H. Havaldar

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  1. S. S. Ittannavar
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Correspondence to S. S. Ittannavar.

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Ittannavar, S.S., Havaldar, R.H. Detection of breast cancer using the infinite feature selection with genetic algorithm and deep neural network. Distrib Parallel Databases 40, 675–697 (2022). https://doi.org/10.1007/s10619-021-07355-w

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