Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency

1997 ◽  
Vol 16 (6) ◽  
pp. 811-819 ◽  
Author(s):  
W.E. Polakowski ◽  
D.A. Cournoyer ◽  
S.K. Rogers ◽  
M.P. DeSimio ◽  
D.W. Ruck ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Difference Of Gaussians ◽  
Feature Saliency ◽  
Computer Aided ◽  
Detection And Diagnosis

Derivative-based feature saliency for computer-aided breast cancer detection and diagnosis

1996 ◽  
Author(s):  
William E. Polakowski ◽  
Steven K. Rogers ◽  
Dennis W. Ruck ◽  
Richard A. Raines ◽  
Jeffrey W. Hoffmeister
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Feature Saliency ◽  
Computer Aided ◽  
Detection And Diagnosis

scholarly journals Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Saleem Z. Ramadan
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
The United States ◽  
Computer Aided Diagnosis ◽  
Current Level ◽  
Breast Cancer Detection ◽  
Cad Systems ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Aided Diagnosis

According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.

Remote Computer-Aided Breast Cancer Detection and Diagnosis System Based on Cytological Images

2014 ◽  
Vol 8 (3) ◽  
pp. 949-964 ◽  
Author(s):  
Yasmeen Mourice George ◽  
Hala Helmy Zayed ◽  
Mohamed Ismail Roushdy ◽  
Bassant Mohamed Elbagoury
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Diagnosis System ◽  
Remote Computer ◽  
Computer Aided ◽  
Detection And Diagnosis
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