Intelligent System for Early Detection and Classification of Breast Cancer: Data Driven Learning

Data driven learning models have not been successfully implemented due to higher rate of false positive in the detection of breast cancer lesions using mammograms. This research aims to decrease the false positive and increase the accuracy and to keep the independent features as they are. The intelligent system designed uses Fuzzy C as the morphological operators to eliminate the undesired elements of a mammogram. The embedded intelligence for breast cancer in this work has increased the accuracy rate of ~96% compared to existing rate of ~91% leading to the average processing time 0.459S with respect to current timing of 0.598S. Data driven model introduced targets on improving the detection of mass lesions by forcing to ignore undesired texture patterns. Hence, the intelligent system improves the positive rate of mammogram screening by decreasing the false positive rate. Data driven point of care system has been built using convolutional neural networks by applying the Rectifier Liner Unit (ReLU) activation function based on the personalized features extracted for early detection and classification of breast cancer.