Accuracy Enhancement in Early Detection of Breast Cancer on Mammogram Images with Convolutional Neural Network (CNN) Methods using Data Augmentation and Transfer Learning

Abstract

The advancement of computer technology has made it possible for computers to imitate the work of the human brain to make decisions that can be used in the healthcare sector. One of the uses is detecting breast cancer by using Machine Learning to increase the sensitivity and or specificity of detection and diagnosis of the disease. Convolutional Neural Network (CNN) is the most commonly used image analysis and classification method in machine learning. This study aims to improve the accuracy of early detection of breast cancer on mammogram images using the CNN method by adding the Data Augmentation and Transfer method. Learning. This study used a mammography image dataset taken from MIAS 2012. The dataset has seven classes with 322 image samples. The results of accuracy tests of early detection process of breast cancer using CNN by utilizing Data Augmentation and Transfer Learning show several findings: Model VGG-16, Model VGG-19, and ResNet-50 produced the same training accuracy rate of 86%, while for validation accuracy, Model ResNet-50 produced the highest level of accuracy (71%) compared to other models (VGG-16=64%, VGG-19=61%). The use of more image datasets may create better accuracy.