Breast Cancer Detection Using Deep Learning

Abstract

The most frequently occurring cancer among Indian women is breast cancer. The second major cause of women's death is breast cancer (after lung cancer). 246,660 of women's new cases of invasive breast cancer are expected to be diagnosed in the US during 2016 and 40,450 of women’s death is estimated. The number of breast cancer cases is steadily growing especially with increasing number of ageing population. Breast cancer is a type of cancer that starts in the breast. Cancer starts when cells begin to grow out of control. Breast cancer cells usually form a tumour that can often be seen on an x-ray or felt as a lump. The screening practice using mammography needs to be better and potentially efficient. There is always room for advancement when it comes to medical imaging. Early detection of cancer can reduce the risk of death for cancer patients. Breast cancer can spread when the cancer cells get into the blood or lymph system and are carried to other parts of the body. This overview looks at the results of a proposed system for automatically diagnosing breast cancer using several convolutional neural network (CNN) designs and compares them to those achieved using machine learning (ML) techniques. All buildings were built from a large database. Validation tests were conducted in order to provide quantifiable results using the performance parameters of each approach. Functions like VGG16 and ResNet50 are used in this model to improve the accuracy of the model. The cause of Breast Cancer includes changes and mutations in DNA. Women are seriously threatened by breast cancer with high morbidity and mortality. The breast cancer dataset is publicly available on the UCI Machine Learning Repository and therefore the implementation phase dataset is going to be partitioned as 80% for the training phase and 20% for the testing phase then apply a significant performance in respect of all parameters.