Improving Methods for Breast Cancer Detection and Diagnosis

2013 ◽  
Author(s):  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
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.

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

Comparison Between Two Novel Approaches in Automatic Breast Cancer Detection and Diagnosis and Its Contribution in Military Defense

2021 ◽  
pp. 189-201
Author(s):  
Jackeline Pereira-Carrillo ◽  
Diego Suntaxi-Dominguez ◽  
Oscar Guarnizo-Cabezas ◽  
Gandhi Villalba-Meneses ◽  
Andrés Tirado-Espín ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Novel Approaches ◽  
Detection And Diagnosis

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

A cost-effective computer-vision based breast cancer diagnosis

2021 ◽  
pp. 1-11
Author(s):  
Prabira Kumar Sethy ◽  
Chanki Pandey ◽  
Dr. Mohammad Rafique Khan ◽  
Santi Kumari Behera ◽  
K. Vijaykumar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computer Vision ◽  
Cancer Detection ◽  
Cost Effective ◽  
Breast Cancer Diagnosis ◽  
Breast Conserving Surgery ◽  
Breast Cancer Detection ◽  
World Health ◽  
Partial Mastectomy ◽  
Detection And Diagnosis

In the last decade, there have been extensive reports of world health organization (WHO) on breast cancer. About 2.1 million women are affected every year and it is the second most leading cause of cancer death in women. Initial detection and diagnosis of cancer appreciably increase the chance of saving lives and reduce treatment costs. In this paper, we perform a survey of the techniques utilized in breast cancer detection and diagnosis in image processing, machine learning (ML), and deep learning (DL). We also proposed a novel computer-vision based cost-effective method for breast cancer detection and diagnosis. Along with the detection and diagnosis of breast cancer, our proposed method is capable of finding the exact position of the abnormality present in the breast that will help in breast-conserving surgery or partial mastectomy. The proposed method is the simplest and cost-effective approach that has produced highly accurate and useful outcomes when compared with the existing approach.

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