Abstract
Intelligent Computer Aided Diagnosis (CAD) Systems can be used for detecting Microcalcification (MC) clusters in digital mammograms at the early stage. CAD systems help radiologists in identifying tumor patterns in an efficient and faster manner than other detection methods. In this paper, we propose a new approach for detecting tumors in mammograms using Radial Basis Function Networks (RBFNN). Prior to the detection of MC clusters features from the image are extracted and analyzed. Gabor features are extracted from the image Region of Interest (ROI) to distinguish a tumor cluster and a normal breast tissue. Once the features are extracted, they are given as input to the supervised RBFNN. The output neuron determines whether the given input ROI is cancer tissue or not. We have verified the algorithm with 322 mammograms in the Mammographic Image Analysis Society Database (MIAS). The results shows that the proposed algorithm has a sensitivity of 85.2%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
