Breast Carcinoma Pigeonholing and Vaticination Using an Interspersed and Malleable Approach

Breast carcinoma is considered as the second major cause of death in females. Malignant tumor affects some tissues of breast and may spread over neighboring tissues. Early detection of this malignant mass is very important to save the precious lives. Although the death rate is reduced by application of modern tools yet research for optimal solutions is still in progress to bring more comprehensive mechanisms. In this paper, we are proposing an interspersed approach for breast tumor pigeonholing and vaticination. We trained our neural network over datasets obtained from the University of Wisconsin Hospitals, Madison and tested over many other datasets with diverse network architectures. The proposed approach was sectioned in applications of data filters. Our network architecture showed 96% of malignant and 99.45% of benign diagnosis for training confusion matrix and 100% for malignant and 97% benign for cross validation matrix. We have given detailed experimentations in light of training and cross validation mean square errors and demonstrated results even for minute curve fluctuations.