Cascade-correlation neural networks for breast cancer diagnosis

This study explores the predictive abilities of the cascade-correlation neural networks as a tool for breast cancer diagnosis. The dataset used for training and testing contains a combination of mammographic, sonographic, and other descriptors, which is novel for the field. We applied feature selection techniques to find an optimal set of descriptors that ensure high sensitivity and specificity. The model performance was estimated by ROC analysis and metrics derived from it, such as max accuracy, full and partial area under the ROC curve and the convex hull, and specificity at 98% sensitivity. Our findings show that particular feature selection techniques applied with the cascade-correlation model outperform the traditional backpropagation networks in all the metrics. The proposed model also provides advantages, such as self-organization of the structure, few parameters to adjust, and fast training, which makes it a better alternative for applications in the domain.