Abstract
In the present work, three-class breast tissue density classification has been carried out using SVM-based hierarchical classifier. The performance of Laws’ texture descriptors of various resolutions have been investigated for differentiating between fatty and dense tissues as well as for differentiation between fatty-glandular and dense-glandular tissues. The overall classification accuracy of 88.2% has been achieved using the proposed SVM-based hierarchical classifier.
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Virmani, J., Kriti, Thakur, S. (2019). Classification of Breast Tissue Density Patterns Using SVM-Based Hierarchical Classifier. In: Hoda, M., Chauhan, N., Quadri, S., Srivastava, P. (eds) Software Engineering. Advances in Intelligent Systems and Computing, vol 731. Springer, Singapore. https://doi.org/10.1007/978-981-10-8848-3_18
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