Claudio Rancan

Tumor Classification on Mammographies Based on BPNN and Sobel Filter

Breast cancer is a very common disease and the cause of death of many people. It has been proven that prevention decreases the death rate, but the costs of diagnosis and image processing are very high when applied to all the population with potential risk. This paper studies an existent computer aided diagnosis method using neural network and improves its detection success rate from 60% to 73%. This improvement is achieved due to the use of image and statistical operators over concentric regions around the tumor boundaries.

Passive Analog Filter Design Using GP Population Control Strategies

This paper presents the use of two different strategies for genetic programming (GP) population growth control: decreasing the computational effort by plagues and dynamic adjustment of fitness; applied to passive analog filters design based on general topologies. Obtained experimental results show that proposed strategies improve the design process performance.