A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography.

Kuen-Jang Tsai,Jung-Hsiu Hsu,Mei-Chun Chou,Shin-Tso Liu,Chao-Ming Hung,Shaw-Hwa Hwang,Cheng-Yu Yeh,Wei-Cheng Yeh,Hao-Ming Li

doi:10.3390/s22031160

Abstract

Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.

Highlights

The incidence rate for breast cancer ranks first [1]
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Each performance metric is a function of thrB and thrL. The outperformance of this model was indicated by an overall accuracy of 94.22%, an average sensitivity of 95.31% and an average specificity of 99.15%

Summary

Introduction

The incidence rate for breast cancer ranks first [1]. A recent report [2] indicates that more than 10,000 Taiwanese women are diagnosed as having breast cancer, and more than 2000 died of breast cancer in 2018. As a matter of fact, treatments for early-stage breast cancer are effective. The 5-year survival rate for stage 0–2 breast cancer exceeds 90%, while it falls below 25% for stage 4 [3]. Screening mammography has been acknowledged as the most reliable way to detect breast cancer at an early stage, in detecting grouped microcalcification lesions. The Taiwanese government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram on a biennial basis. A great number of mammograms are collected in a large-scale mammography screening program and need to be interpreted by well-qualified but overloaded radiologists. There is definitely an unmet need to develop AI models to assist radiologists with mammographic interpretation, and AI model development requires interdisciplinary research that integrates medical science and engineering

Methods

Results

Conclusion