Breast Cancer Diagnosis in Women Using Neural Networks and Deep Learning

Abstract

Breast cancer is a deadly disease affecting women around the world. It can spread rapidly into other parts of the body, causing untimely death when undetected due to rapid growth and division of cells in the breast. Early diagnosis of this disease tends to increase the survival rate of women suffering from the disease. The use of technology to detect breast cancer in women has been explored over the years. A major drawback of most research in this area is low accuracy in the detection rate of breast cancer in women. This is partly due to the availability of few data sets to train classifiers and the lack of efficient algorithms that achieve optimal results. This research aimed to develop a model that uses a machine learning approach (convolution neural network) to detect breast cancer in women with significantly high accuracy. In this paper, a model was developed using 569 mammograms of various breasts diagnosed with benign and maligned cancers. The model achieved an accuracy of 98.25% and sensitivity of 99.5% after 80 iterations.