Abstract
Author propose mammogram classification technique to classify the breast tissues as benign or malignant. Mammogram is segmented to obtain Region of Interest (ROI) and 2D DWT is obtained. GLCM feature matrix is generated for all the detailed coefficient of 2D DWT. Optimum Feature Decomposition Algorithm (OFDA) is used to discretize and optimize the features. Author propose Optimum Decomposition Selection Algorithm (ODSA) to select optimum decomposition from nine multiresolution wavelet decompositions of ROI using Euclidean distance between the feature matrixes. High-dimensional future space may degrade the performance of the classifier. Using propose algorithm, the size of feature matrix reduces to [N × F]T from [(N × 9) × F]T. Hence, dimension of search space reduces by approximately 90%. From the optimized feature vector and optimized decomposition, a signature feature vector matrix consisting of optimum decomposition and its optimum feature vector is generated to form transactional database. Association rules are generated using Apriori algorithm. These rules are optimized using multiobjective Genetic Algorithm with adaptive crossover and mutation. Mammogram is classified using Class Identification using Strength of Classification (CISCA) algorithm. The results are tested on two standard databases: MIAS and DDSM. It is noted that the propose scheme has advantage in terms of accuracy and computational complexity of the classifier.
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Sonar, P., Bhosle, U. (2019). Transform Domain Mammogram Classification Using Optimum Multiresolution Wavelet Decomposition and Optimized Association Rule Mining. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_56
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