Transition of traditional method to deep learning based computer-aided system for breast cancer using Automated Breast Ultrasound System (ABUS) images: a review

被引：0
作者：
Dayangku Nur Faizah Pengiran Mohamad
Syamsiah Mashohor
Rozi Mahmud
Marsyita Hanafi
Norafida Bahari
机构：
[1] University Putra Malaysia (UPM),Department of Computer & Communication Systems Engineering, Faculty of Engineering
[2] University Putra Malaysia (UPM),Radiology Department, Faculty of Medicine & Health Science
来源：
Artificial Intelligence Review | 2023年 / 56卷
关键词：
Breast cancer; Automated Breast Ultrasound System (ABUS); Deep learning; Computer- aided (CAD) system;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Breast cancer (BC) is the leading cause of death among women worldwide. Early detection and diagnosis of BC can help significantly reduce the mortality rate. Ultrasound (US) can be an ideal screening tool for BC detection. However, the hand-held US (HHUS) is an impractical tool because it is operator-dependent, time-consuming, and increases the likelihood of false-positive results. Thus, to address these issues, the 3D Automated Breast Ultrasound System (ABUS) was designed for BC detection and diagnosis. This paper presents the transition from traditional approaches to deep learning (DL) based CAD systems in the ABUS image data set. The capabilities and limitations of both techniques are also reviewed rigorously. This review will help in understanding the current limitations to leverage their potential in diagnostic radiology to improve performance and BC patient care.
引用
收藏
页码：15271 / 15300
页数：29
相关论文
共 362 条
[1]  
Badawy SM(2021)Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study PLoS ONE 16 487-489
[2]  
Mohamed AENA(2020)Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers from the radiology editorial board Radiology 294 703-1609
[3]  
Hefnawy AA(2021)Pros and cons for automated breast ultrasound (ABUS): a narrative review J Person Med 11 1604-1903
[4]  
Zidan HE(2016)A review of supplemental screening ultrasound for breast cancer: certain populations of women with dense breast tissue may benefit Acad Radiol 23 1895-1546
[5]  
GadAllah MT(2018)Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images Int J Comput Assist Radiol Surg 13 15-2073
[6]  
El-Banby GM(2020)A review of the application of deep learning in medical image classification and segmentation Ann Transl Med 8 1532-1170
[7]  
Bluemke DA(2022)Auto-DenseUNet: Searchable neural network architecture for mass segmentation in 3D automated breast ultrasound Med Image Anal 82 2063-317
[8]  
Moy L(2007)Adaptive wavelet thresholding for image denoising and compression IEEE Trans Image Process 9 1163-249
[9]  
Bredella MA(2010)Rapid image stitching and computer-aided detection for multipass automated breast ultrasound Med Phys 37 299-2229
[10]  
Ertl-Wagner BB(2015)Automated Breast Ultrasound System (ABUS): reproducibility of massclocalization, size measurement, and characterization on serial examinations Acta Radiol 56 240-16
12345678910