New Methods for the Early Detection and Diagnosis of Breast Cancer

1984 ◽  
Vol 73 (2) ◽  
pp. 549-552 ◽  
Author(s):  
Donald E. Henson
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
New Methods ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

scholarly journals A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Jinyu Cong ◽  
Benzheng Wei ◽  
Yunlong He ◽  
Yilong Yin ◽  
Yuanjie Zheng
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Ensemble Classification ◽  
Classification Method ◽  
Ultrasound Images ◽  
Selective Ensemble ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Breast cancer has been one of the main diseases that threatens women’s life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning.

scholarly journals AN IN-DEPTH ANALYSIS OF THE IDENTIFIED ALGORITHMS AND THEIR COMPARATIVE STUDY IN THE EARLY DETECTION AND DIAGNOSIS OF BREAST CANCER

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mridul Sharma
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Early Detection ◽  
Healthcare Workers ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Depth Analysis ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

These days one of the major inevitable ailments for females is bosom malignancy. The appropriate medication and early findings are important stages to take to thwart this ailment. Although, it's not easy to recognize due to its few vulnerabilities and lack of data. Can use artificial intelligence to create devices that can help doctors and healthcare workers to early detection of this cancer. In This research, we investigate three specific machine learning algorithms widely used to detect bosom ailments in the breast region. These algorithms are Support vector machine (SVM), Bayesian Networks (BN) and Random Forest (RF). The output in this research is based on the State-of-the-art technique.

Project Sasha

2021 ◽  
Vol 9 (VI) ◽  
pp. 1479-1489
Author(s):  
Aparna Mete Sawant
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Detection Algorithm ◽  
Test Results ◽  
Registration Process ◽  
History Of ◽  
Haar Cascade ◽  
Detection And Diagnosis ◽  
Virtual Platform ◽  
Early Detection And Diagnosis

This paper presents an application that aids in the early detection and diagnosis of breast cancer in women, efficiently and accurately. Furthermore, the application eliminates the need for direct contact between patient and doctor by providing a virtual platform in the form of a GUI wherein the patient can upload scanned copies of test results as prescribed by an oncologist. The digitization of the registration process is done via face recognition using Haar Cascade. The application in this paper provides a platform for the doctors to- write a new prescription, view appointments, access reports, view the history of every patient; for patients to- book an appointment, view their prescriptions, access reports and review previous appointments; for pharmacists to view the prescription of a particular patient. The link between patients, doctors and pharmacists is highlighted in the proposed application. The latest object detection algorithm YOLOv3 is used for early detection of breast cancer after the image is annotated. After the training and testing, the model gives an accuracy between (75- 80)%.

scholarly journals Fuzzy relational model and genetic algorithms for early detection and diagnosis of breast cancer in Saudi Arabia

Filomat ◽  
2016 ◽  
Vol 30 (3) ◽  
pp. 547-556 ◽  
Author(s):  
Fairouz Tchier ◽  
Abir Alharbi
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithms ◽  
Saudi Arabia ◽  
Early Detection ◽  
Computing Methodology ◽  
Fitness Function ◽  
Relational Model ◽  
Breast Cancer Dataset ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Breast Cancer (BC) is considered as the most implacable malignancy and the leading cause of mortality among women in general and in Saudi Arabia specially. Most of the previous work in Saudi Arabia on this subject was on epidemiology, knowledge of (BC) and practice of breast self-examination (BSE), etiological factors, metastases and rate of survival. Early detection and diagnosis of Breast Cancer (BC) is an important, real-world medical problem. In this paper, we propose a soft computing methodology to build a Breast Cancer (BC) diagnosis system with high capabilities as described by Andres et al. [4] but on the Saudi Arabian breast cancer dataset and using a simplified fitness function. We focus on combining fuzzy concepts and genetic algorithms so as to automatically produce diagnostic systems to support and assist the expert to understand and evaluate its results with high classification performance.

Detection of Leukemia Using Image Processing

2018 ◽  
Vol 8 (4) ◽  
pp. 61
Author(s):  
A. Sivasangari ◽  
G. Sasikumar
Keyword(s):  
Image Processing ◽  
Statistical Analysis ◽  
Early Detection ◽  
Visual Inspection ◽  
Causes Of Death ◽  
Cure Rate ◽  
Blood Disorders ◽  
Detection And Diagnosis ◽  
The Moment ◽  
Early Detection And Diagnosis

Leukemia   disease   is one   of    the   leading   causes   of death   among   human. Its  cure  rate and  prognosis   depends   mainly   on  the  early  detection   and  diagnosis  of   the  disease. At  the  moment, identification  of  blood  disorders  is  through   visual  inspection  of  microscopic  images  by  examining  changes  like  texture, geometry, colour  and   statistical  analysis  of  images . This  project  aims  to  preliminary  of  developing  a  detection  of  leukemia  types  using   microscopic  blood  sample using MATLAB. Images  are  used  as  they  are  cheap  and  do  not  expensive  for testing  and  lab  equipment.

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