Publication Type : Conference Paper
Publisher : Springer Singapore
Source : The 16th International Conference on Biomedical Engineering, Springer Singapore, Singapore (2017)
Url : https://link.springer.com/chapter/10.1007/978-981-10-4220-1_13
ISBN : 9789811042201
Campus : Coimbatore
School : School of Engineering
Center : Electronics Communication and Instrumentation Forum (ECIF)
Department : Electronics and Communication, Communication
Verified : Yes
Year : 2017
Abstract : Breast cancer is the major cancer diagnosed in both, developed and developing countries. Early detection and treatment of breast cancer is necessary to moderate the associated fatality rates. Mammography is the widely accepted modality for screening breast cancer. Breast density is considered one of the major risk indicators for Breast cancer. Nevertheless, low contrast and subtle nature of abnormalities reduces the sensitivity of mammograms, especially in dense breast. In this paper we present an automatic method for breast density classification based on two level cascaded support vector machine (SVM) classifiers. Particle Swarm Optimization (PSO) has been employed for SVM parameter optimization that resulted in a low set up time for building the system. The proposed system was tested on mini-MIAS database, and an overall classification accuracy of 82% was achieved. Also the system could prompt the radiologists on high-risk cases, thereby gaining more attention from them for diagnosis of such cases.
Cite this Research Publication : S. Simon, Dr. Lavanya R., and Vijayan, D., “PSO Based Density Classifier for Mammograms”, in The 16th International Conference on Biomedical Engineering, Singapore, 2017.