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.

Laplacian Metropolis Hastings Gradient Torgerson Scaling and Optimized Vgg16 Based Mammogram Classification

“…With this type of gene analysis improved breast cancer detection results efficiently. Yet another boosting mechanism for accurate improvement of breast cancer detection was designed in [18]. However there still remains room for enhancing prevailing CAD method by combining new algorithms for providing accurate results as far as possible.…”

Breast Cancer Early Detection Comparison with Deep Learning and Machine Learning Models: A Case of Study

“…The authors start from the histopathological images of breast cancer using the BreaKHis data set: they normalize the data and make a data augmentation of the spots for pre-processing, the task is to classify 8 types of classes, obtaining an accuracy of the 97% for both binary and multiclassification. Another fairly recent work combining deep learning and XGboost is that of Sugiharti, et al [10] who use a Convolutional Neural Network (CNN) combined with an XGBoost as a classifier. The phases of the research method proposed by the authors are divided as follows: collection of the MIAS 2012 dataset, split of data in train and test (70% and 30% respectively), data pre processing, data augmentation and transfer learning and then classify using the combination of the two models.…”