MultiNet: A deep neural network approach for detecting breast cancer through multi-scale feature fusion

Abstract

Breast cancer diagnosis from biopsy tissue images conducted manually by pathologists is costly, time-consuming, and disagreements among specialists. Nowadays, the advancement of the Computer-Aided Diagnosis (CAD) system allows pathologists to identify breast cancer more reliably and quickly.For this reason, interest in CAD-based deep learning models has been increased significantly. In this study, we propose a “MultiNet” framework based on the transfer learning concept to classify different breast cancer types using two publicly available datasets that include 7909 and 400 microscopic breast images, respectively. The proposed “MultiNet” framework is designed to provide fast and accurate diagnostics for breast cancer with binary classification (benign and malignant) and multi-class classification (benign, in situ, invasive, and normal). In the proposed framework, features from microscopy images are extracted using three well-known pre-trained models, including DenseNet-201, NasNetMobile, and VGG16. The extracted features are then fed into the concatenate layer, making a robust hybrid model. The proposed framework yields an overall classification accuracy of 99% in classifying two classes. It also achieves 98% classification accuracy in classifying four classes. Such promising results will provide the opportunity to use “MultiNet” framework as a diagnostic model in clinics and health care.

Keywords

• Breast cancer
• CAD
• DenseNet-201
• NasNetMobile
• VGG16
• Feature fusion