Applied Deep Learning Architectures for Breast Cancer Screening Classification

Abstract

Breast cancer (BC) became the most diagnosed cancer, making it one of the deadliest diseases. Mammography is a modality used for early detection of breast cancer. The objective of the present paper is to evaluate and compare deep learning techniques applied to mammogram images. The paper conducts an experimental evaluation of eight deep Convolutional Neural Network (CNN) architectures for a binary classification of breast screening mammograms, namely VGG16, VGG19, DenseNet201, Inception ResNet V2, Inception V3, ResNet 50, MobileNet V2 and Xception. This evaluation was based on four performance metrics (accuracy, precision, recall and f1-score), as well as Scott Knott statistical test and Borda count voting system. The data was extracted from the CBIS-DDSM dataset with 4000 images. And results have shown that DenseNet201 was the most efficient model for the binary classification with an accuracy of 84.27%.