Implementing combined neural network model for breast cancer diagnosis
İnan Güler, Elif Derya Übeylı · Bulletin of the International Scientific Surgical Association · 2006
This paper illustrates the use of combined neural network (CNN) models to guide model selection for breast cancer diagnosis. Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. Specifically, diagnosis is an attempt to accurately forecast the outcome of a specific situation, using as input information obtained from a concrete set of variables that potentially describe the situation. The CNN network model trained with Levenberg-Marquardt algorithm used the attributes of each record in the Wisconsin breast cancer database. The first level networks were implemented for the diagnosis of breast cancer using the attributes of each record as inputs. To improve diagnostic accuracy, the second level networks were trained using the outputs of the first level networks as input data. For the Wisconsin breast cancer diagnosis problem, the obtained total classification accuracy by the CNN network model was 98.15%. The CNN network model achieved accuracy rates which were higher than that of the stand-alone neural network models.