Mammographic image classification with deep fusion learning

• Published in: Scientific reports Vol. 10; no. 1; p. 14361
• Main Authors: Yu, Xiangchun, Pang, Wei, Xu, Qing, Liang, Miaomiao
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01-09-2020; Nature Publishing Group
• Subjects: 631/67/1347; 639/624/1075; 639/624/400/1106; Allergic rhinitis; Breast - diagnostic imaging; Breast Neoplasms - classification; Breast Neoplasms - diagnostic imaging; Deep Learning; Early Detection of Cancer - methods; Female; Humanities and Social Sciences; Humans; Image Processing, Computer-Assisted - methods; Immunoglobulin E; Immunotherapy; Interleukin 10; Lymphocytes B; Lymphocytes T; Mammography - methods; Metabolites; multidisciplinary; Patients; Polarization; Ribonucleic acid; RNA; Science; Science (multidisciplinary); Serum levels; Tumors; Vitamin D3
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-71431-x

To better address the recognition of abnormalities among mammographic images, in this study we apply the deep fusion learning approach based on Pre-trained models to discover the discriminative patterns between Normal and Tumor categories. We designed a deep fusion learning framework for mammographic image classification. This framework works in two main steps. After obtaining the regions of interest (ROIs) from original dataset, the first step is to train our proposed deep fusion models on those ROI patches which are randomly collected from all ROIs. We proposed the deep fusion model (Model1) to directly fuse the deep features to classify the Normal and Tumor ROI patches. To explore the association among channels of the same block, we propose another deep fusion model (Model2) to integrate the cross-channel deep features using 1 × 1 convolution. The second step is to obtain the final prediction by performing the majority voting on all patches' prediction of one ROI. The experimental results show that Model1 achieves the whole accuracy of 0.8906, recall rate of 0.913, and precision rate of 0.8077 for Tumor class. Accordingly, Model2 achieves the whole accuracy of 0.875, recall rate of 0.9565, and precision rate 0.7,586 for Tumor class. Finally, we open source our Python code at
https://github.com/yxchspring/MIAS
in order to share our tool with the research community.