Breast cancer diagnosis using SE-DNI model
Xinran Gu, Yurong Liu, Quansen Wang, Yuzhi Zhang · Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering · 2021
Breast cancer has long been a life-threatening disease, and the application of digital pathological detection systems on breast cancer detection, which employs convolutional neural networks (CNN), is a milestone in the medical field. For medical testing, higher accuracy means a stronger possibility of saving ones’ life. However, the accuracy of the current study on mammography image classification is not satisfying enough. In this paper, we simplified and improved the current network structure's performance on classifying histopathological images by proposing a new Squeeze and Excitation network DenseNet based improved model (SE-DNI), which is a hierarchical multi-stage process that consists of six layers. We tested three different DenseNet models for feature extraction, and our improved model of DenseNet201 with attention layer (SENet) has the highest test score among all three models we tested, which achieved an accuracy of 99.82% compared with 96.15%, the accuracy of the network that uses a normal DenseNet201 model.