scholarly journals Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review

10.2196/14464 ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. e14464 ◽  
Author(s):  
Syed Jamal Safdar Gardezi ◽  
Ahmed Elazab ◽  
Baiying Lei ◽  
Tianfu Wang
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Data Augmentation ◽  
Computational Cost ◽  
Feature Learning ◽  
Breast Cancer Diagnosis ◽  
Clinical Analysis ◽  
Learning Ability ◽  
Cad Systems ◽  
Imaging Research

Background Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems. Objective This review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks. Methods In this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets. Results The analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems. Conclusions From the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.

scholarly journals A Research on Computer Aided Detection System for Women Breast Cancer Diagnosis from Digital Mammographic Images

2019 ◽  
Vol 8 (2S11) ◽  
pp. 1008-1014
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Death Rate ◽  
Computational Cost ◽  
Breast Cancer Diagnosis ◽  
Computer Aided Detection ◽  
Cad Systems ◽  
Cad System ◽  
Mammographic Images ◽  
Computer Aided

The Women breast cancer is the most critical cancer that are found in women. Its the second important cause of death in the world. Breast cancer has been ranked number one cancer in Indian females with rates occurrence of 25.8 per 1,00,000 females and death rate 12.7 among 1,00,000. Generally breast cancer is a malignant tumor that begins in the cells of the breast and eventually it spreads to the surrounding tissues. Early detection and diagnosis can reduce the mortality rate. Radiologist misdiagnosis the disease due to technical issues such as imaging quality and human error. Radiologists can improve the performance of Computer Aided Detection/Diagnosis (CAD) systems to finding and discriminating between the normal and abnormal tissues. Breast cancer diagnosis can applied are applied recent CAD systems on imaging modalities such as mammogram, ultrasound, MRI and biopsy histopathological images. CAD system have four stages for diagnosis which are pre-processing, segmentation, Feature Extraction and Classification. CAD system are developed to reduce the time taken to diagnose the breast cancer and reduce the death rate. This paper focus on the survey of CAD system to detect women breast cancer disease from the digital mammographic images to achieve high accuracy and low computational cost.

Breast Cancer Diagnosis With Mammography

2021 ◽  
pp. 107-127
Author(s):  
Abir Baâzaoui ◽  
Walid Barhoumi
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Gold Standard ◽  
Breast Cancer Diagnosis ◽  
Black Box ◽  
Diagnose Breast Cancer ◽  
Cad Systems ◽  
Helpful Tool ◽  
Cancerous Lesion ◽  
Aided Diagnosis

Breast cancer, which is the second-most common and leading cause of cancer death among women, has witnessed growing interest in the two last decades. Fortunately, its early detection is the most effective way to detect and diagnose breast cancer. Although mammography is the gold standard for screening, its difficult interpretation leads to an increase in missed cancers and misinterpreted non-cancerous lesion rates. Therefore, computer-aided diagnosis (CAD) systems can be a great helpful tool for assisting radiologists in mammogram interpretation. Nonetheless, these systems are limited by their black-box outputs, which decreases the radiologists' confidence. To circumvent this limit, content-based mammogram retrieval (CBMR) is used as an alternative to traditional CAD systems. Herein, authors systematically review the state-of-the-art on mammography-based breast cancer CAD methods, while focusing on recent advances in CBMR methods. In order to have a complete review, mammography imaging principles and its correlation with breast anatomy are also discussed.

A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI

2020 ◽  
Vol 69 ◽  
pp. 40-48
Author(s):  
Hongwei Feng ◽  
Jiaqi Cao ◽  
Hongyu Wang ◽  
Yilin Xie ◽  
Di Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Integration Method ◽  
Feature Learning ◽  
Breast Cancer Diagnosis

scholarly journals Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

2020 ◽  
Vol 10 (3) ◽  
pp. 2336 ◽  
Author(s):  
Susama Bagchi ◽  
Kim Gaik Tay ◽  
Audrey Huong ◽  
Sanjoy Kumar Debnath
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Imaging ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Computer Aided Detection ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.

Physical Activity and Survival Subsequent to Metastatic Breast Cancer Diagnosis

2010 ◽  
Author(s):  
Susan Sharp ◽  
Ashleigh Golden ◽  
Cheryl Koopman ◽  
Eric Neri ◽  
David Spiegel
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Metastatic Breast Cancer ◽  
Cancer Diagnosis ◽  
Metastatic Breast ◽  
Breast Cancer Diagnosis
Sign in / Sign up

Export Citation Format

Share Document