Convolutional neural Network-XGBoost for accuracy enhancement of breast cancer detection

Abstract

Computer programs can work by imitating the human brain to make decisions that can be used in the health sector. One of them is the Convolutional Neural Network (CNN) which is combined with XGBoost as the classifier. CNN-XGBoost can be implemented for the accuracy of early detection of breast cancer. The problem is how to improve the accuracy of breast cancer detection on mammogram images. The stages of the research method: (1) Collecting the MIAS 2012 dataset, (2) Dividing data into training data and testing data. (3) preprocessing: cropping, resizing, and reshaping. (4) Data Augmentation (5) Transfer Learning (6) Classification using CNN-XGBoost (7) Testing the accuracy. Based on the research that has been carried out, the results are: (1) Obtained data on the accuracy of using CNN-XGBoost on mammogram image analysis in early detection of breast cancer. (2) Further testing is needed to improve accuracy. Further testing is needed with the use of other method or by improving the quality of mammogram images.