دورية أكاديمية
Convolutional Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening for Vietnamese patients
| العنوان: | Convolutional Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening for Vietnamese patients |
|---|---|
| المؤلفون: | Bui My Hanh, Le Tuan Linh, Nguyen Ngoc Cuong, Thanh Binh Nguyen, Luu Tien Doan, Chung Duy Le, Vu Tat Giao, Thi Ly Ly Ngo, Thi Hong Xuyen Hoang, Nguyen Duc Thang, Nguyen Tu Anh, Nguyen Duc Dan, Nguyen Viet Dung, Tran Vinh Duc, Quang H. Nguyen, Anh Nguyen, Nguyen Hoang Phuong |
| المصدر: | Applied Artificial Intelligence, Vol 36, Iss 1 (2022) |
| بيانات النشر: | Taylor & Francis Group, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Electronic computers. Computer science LCC:Cybernetics |
| مصطلحات موضوعية: | Electronic computers. Computer science, QA75.5-76.95, Cybernetics, Q300-390 |
| الوصف: | Nowadays, breast cancer is one of the leading cancers in Vietnam, and it causes approximately 6000 deaths every year. The rate of breast cancer patients was calculated as 26.4/100000 persons in 2018. There are 21,555 new cases reported in 2020. However, these figures can be reduced with early detection and diagnosis of breast cancer disease in women through mammographic imaging. In many hospitals in Vietnam, there is a lack of experienced breast cancer radiologists. Therefore, it is helpful to develop an intelligent system to improve radiologists’ performance in breast cancer screening for Vietnamese patients. Our research aims to develop a convolutional neural network-based system for classifying breast cancer X-Ray images into three classes of BI-RADS categories as BI-RADS 1 (“normal”), BI-RADS 23 (“benign”) and BI-RADS 045 (“incomplete and malignance”). This classification system is developed based on the convolutional neural network with ResNet 50. The system is trained and tested on a breast cancer image dataset of Vietnamese patients containing 7912 images provided by Hanoi Medical University Hospital radiologists. The system accuracy uses the testing set achieved a macAUC (a macro average of the three AUCs) of 0.754. To validate our model, we performed a reader study with the breast cancer radiologists of the Hanoi Medical University Hospital, reading about 500 random images of the test set. We confirmed the efficacy of our model, which achieved performance comparable to a committee of two radiologists when presented with the same data. Additionally, the system takes only 6 seconds to interpret a breast cancer X-Ray image instead of 450 seconds interpreted by a Vietnamese radiologist. Therefore, our system can be considered as a “second radiologist,” which can improve radiologists’ performance in breast cancer screening for Vietnamese patients. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 0883-9514 1087-6545 08839514 |
| Relation: | https://doaj.org/toc/0883-9514; https://doaj.org/toc/1087-6545 |
| DOI: | 10.1080/08839514.2022.2151185 |
| URL الوصول: | https://doaj.org/article/76a4d2fef24943efbd974ad7bf2bd512 |
| رقم الأكسشن: | edsdoj.76a4d2fef24943efbd974ad7bf2bd512 |
| قاعدة البيانات: | Directory of Open Access Journals |
PDF full text from Publisher 
Full Text Finder | تدمد: | 08839514 10876545 |
|---|---|
| DOI: | 10.1080/08839514.2022.2151185 |