Advertisement

Gaussian Light Gradient Boost Ensemble Decision Tree Classifier for Breast Cancer Detection

Conference paper
  • 223 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 118)

Abstract

Detection of cancer in the breasts shows an important role in minimizing the mortality rates and increasing the cure rate, relieve as well as guarantee the patient’s life quality. Several works have been done in the breast cancer detection but it failed to perform accurate detection with minimum time. In order to improve breast cancer detection, an ensemble technique called Gaussian light gradient boost decision tree classification (GLGBDTC) is introduced. Initially, images are collected from the database. The Light Gradient Boost technique further constructs a number of base classifiers namely c4.5 decision trees using Kullback–Leibler divergence value, by which the data are classified and the results are to be sum up for making strong classification outcomes. For all the base classifiers, the similar weights are assigned. Then the Gaussian training loss is computed for each base classifier results. Followed by, the weight is updated according to the loss value. The steepest descent function is used to discover best classifier with minimum training loss. By this way, the proposed technique performs accurate breast cancer detection. The simulation results show minimize false positive rate (FPR).

Keywords

Light gradient boost Base classifiers Kullback–Leibler divergence value c4.5 decision tree Gaussian training loss Steepest descent function 

References

  1. 1.
    Zhang X, Zhang Y, Han EY, Jacobs N, Han Q, Wang X, Liu J (2018) Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Trans NanoBiosci 17(3)237–242Google Scholar
  2. 2.
    Gao F, Wu T, Li J, Zheng B, Ruan L, Shang D, Patel B (2018) SD-CNN: a shallow-deep CNN for improved breast cancer diagnosis. Comput Med Imaging Graph 70:53–62 (Elsevier)Google Scholar
  3. 3.
    Reis S, Gazinska P, Hipwell JH, Mertzanidou T, Naidoo K, Williams N, Pinder S, Hawkes DJ (2017) Automated classification of breast cancer stroma maturity from histological images. IEEE Trans Biomed Eng 64(10):2344–2352Google Scholar
  4. 4.
    Gecer B, Aksoy S, Mercan E, Shapiro LG, Weaver DL, Elmore JG (2018) Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern Recogn 84:345–356 (Elsevier)Google Scholar
  5. 5.
    Liang C, Bian Z, Lv W, Chen S, Zeng D, Ma J (2018) A computer-aided diagnosis scheme of breast lesion classification using GLGLM and shape features: combined-view and multi-classifiers. Physica Med 55:61–72 (Elsevier)Google Scholar
  6. 6.
    Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A (2016) Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 35(1):119–130Google Scholar
  7. 7.
    do Nascimento MZ, Martins AS, Neves LA, Ramos RP, Flores EL, Carrijo GA (2013) Classification of masses in mammographic image using wavelet domain features and polynomial classifier. Expert Syst Appl 40:6213–6221 (Elsevier)Google Scholar
  8. 8.
    Pereira DC, Ramos RP, do Nascimento MZ (2014) Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Comput Methods Programs Biomed 114(1):88–101 (Elsevier)Google Scholar
  9. 9.
    Dong M, Wang Z, Dong C, Mu X, Ma Y (2017) Classification of region of interest in mammograms using dual contourlet transform and improved KNN. J Sens 1–15 (Hindawi)Google Scholar
  10. 10.
    Digital Database for Screening Mammography (DDSM). http://marathon.csee.usf.edu/Mammography/Database.html

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Research Scholar, Department of Computer ScienceVels Institute of Science, Technology and Advanced Studies, (VISTAS)ChennaiIndia
  2. 2.Associate Professor, Department of Computer Science and ApplicationsSRM Institute for Training and DevelopmentChennaiIndia
  3. 3.Assistant Professor, Department of Information Technology, School of Computing SciencesVels Institute of Science, Technology and Advanced Studies, (VISTAS)ChennaiIndia

Personalised recommendations