Abstract
Authors presents an effective breast mammogram classification technique by the application of computer assisted tools for augmenting human functions, namely radiologists. Authors present AdaBoost with RBFSVM and Hybrid KNN–RBFSVM as base estimator by adaptively adjusting kernel parameters in RFBSVM (γ and C). KNN–RBFSVM classifier is developed by adaptively adjusting the kernel parameters of SVM using optimized parameters of KNN. In the proposed Hybrid KNN–RBFSVM algorithm, first weighted KNN is applied on training mammograms. Initially equal weights are assigned to each mammogram and updated weights are found. The updated weights from KNN are used as initial weights for RBFSVM. This Hybrid KNN–RBFSVM algorithm is used as base estimator in AdaBoost with number of estimators = 200, for prediction of test mammogram as benign or malignant. GLCM features are extracted from mammograms. Inconsistent and irrelevent features of mammogram affect the classification accuracy. Authors use proposed PreARM algorithm for features optimization. The classification results of the AdaBoost with DT, KNN, RBFSVM and Hybrid KNN–RBFSVM as base estimator are compared in terms of accuracy and area under ROC curve value. DDSM mammogram image dataset is used for experiment. The result shows, AdaBoost with Hybrid KNN–RBFSVM as base estimator achieves improved accuracy and AUC value compared to above ensembles.
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Abbreviations
- DT:
-
Decision tree
- KNN:
-
K nearest neighbor
- RBF:
-
Radial basis function
- SVM:
-
Support vector machine
- GLCM:
-
Grey level co-occurrence matrix
- PreARM:
-
Pre-processing step for association rule mining
- AUC:
-
Area under curve
- ROC:
-
Receiver operating characteristic curve
- DDSM:
-
Digital database for screening mammography
- ROI:
-
Region of interest
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Acknowledgements
The authors wish to express sincere gratitude to Dr. Thomas Deserno and co-authors, for permitting the use of IRMA VERSION OF DDSM LJPEG DATA for research work.
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Bhosle, U., Deshmukh, J. Mammogram classification using AdaBoost with RBFSVM and Hybrid KNN–RBFSVM as base estimator by adaptively adjusting γ and C value. Int. j. inf. tecnol. 11, 719–726 (2019). https://doi.org/10.1007/s41870-018-0241-x
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DOI: https://doi.org/10.1007/s41870-018-0241-x