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Date Published: 30/11/2021
Online Published: 21/02/2023
Abstract Views: 174
Views PDF: 86
Issue
No. 1 (2021)
Section
INFORMATION - FORUM
How to Cite
Bui, M. H., Le, T. L., Nguyen, N. C., Nguyen, T. B., Luu, T. D., Le, D. C., Giao, T. V., Ngo, T. L. L., Hoang , T. H. X., Nguyen, D. T., Nguyen, T. A., Nguyen, D. D., Nguyen, V. D., Tran, V. D., Hong Quang, Nguyen, A., & Nguyen, H. P. (2023). DEVELOPING A SUPPORT SYSTEM FOR BREAST CANCER X-RAY DIAGNOSIS BASED ON ARTIFICIAL INTELLIGENCE. Thang Long Journal of Science: Applied Science, 1, 165 - 184. https://science.thanglong.edu.vn/index.php/vola/article/view/21

DEVELOPING A SUPPORT SYSTEM FOR BREAST CANCER X-RAY DIAGNOSIS BASED ON ARTIFICIAL INTELLIGENCE

My Hanh Bui, Tuan Linh Le, Ngoc Cuong Nguyen, Thanh Binh Nguyen, Tien Doan Luu, Duy Chung Le, Tat Vu Giao, Thi Ly Ly Ngo, Thi Hong Xuyen Hoang , Duc Thang Nguyen, Tu Anh Nguyen, Duc Dan Nguyen, Viet Dung Nguyen, Vinh Duc Tran, Hong Quang , Anh Nguyen, Hoang Phuong Nguyen

Main Article Content

Abstract

In recent years breast cancer has become one of the most popular cancers for women in many countries in the world, especially, in Vietnam. In order to early detect breast cancer in early stages, the mammograms classification for Vietnamese patients in breast cancer screening based on X-ray is considered as an important task. In this paper, we present a method for classifying mammograms of Vietnamese patients into three categories of BI-RADS: BI-RADS 1: breast is normal, BI-RADS 23: (according to BI-RADS 2 to BI-RADS 3): breast is abnormal with benign mass and BI-RADS 045: cannot evaluate a hurt level in breast cancer X-ray image, additional examinations are required or the breast has a hurt with high malignant rate.
Our mammograms classification system for breast cancer screening is developed based on convolutional neural networks (CNNs) with ResNet50 deep-learning method as the basic framework.
The system is trained and tested on an X-ray dataset of Vietnamese patients containing 7,912 mammograms provided by radiologists of the Hanoi Medical University Hospital. The system accuracy using the testing set achieved a combined area under the curve (mac AUC) of 0.75. To evaluate our model, we conducted a reader study with radiologists of the Department of Radiology, Hanoi Medical University Hospital. Clinical evaluation of the mammograms classification system with about 500 random mammograms of the test set shows that the system performance is more accurate compared to that of one random radiologist and it has the approximate accuracy as “committee of radiologists” when presented with the same images. With this result, our system can be considered a “second radiologist” which can help radiologists in mammograms classification of Vietnamese patients for breast cancer screening in Vietnam.

Article Details

Keywords

Ảnh X-quang ung thư vú, Loại BI-RADS, Mạng nơ ron tích chập ResNet, Học sâu

References

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