SDCA: System to Detect Cancerous Abnormalities.
Eddy Sánchez de la Cruz, Homero Alpuín-Jiménez, Humberto de Jesús Ochoa Domínguez, Pilar Pozos Parra · 2011
In this article we present SDCA, which is a system to detect cancerous abnormalities in digital mammograms. The SDCA try to give at radiologist a second opinion in the analysis of a digital mammogram to increase the reliability of detecting breast cancer. SDCA is a semiautomation of KDD process (Knowledge Discovery in Databases). The KDD process is a method that uses strategies of Artificial Intelligence (AI) to extract patterns of behavior in databases with large volumes of information. Two SDCA characteristics outstanding are 1) the implementation of Mejia filtering method in the data cleansing module, and 2) the implementation of Decorate strategy Classification in the classification module. The results shows that SDCA get 95% of detections classified correctly. SDCA was developed using Matlab GUIDE, and tests were done with the database (DB) of digital mammographic MIAS.