scholarly journals A big data management approach for computer aided breast cancer diagnostic system supporting precision medicine

2019 ◽  
Author(s):  
Veska Gancheva
Keyword(s):  
Breast Cancer ◽  
Big Data ◽  
Data Management ◽  
Precision Medicine ◽  
Diagnostic System ◽  
Management Approach ◽  
Cancer Diagnostic ◽  
Computer Aided

scholarly journals Agile Project Management Approach and its Use in Big Data Management

2016 ◽  
Vol 83 ◽  
pp. 576-583 ◽  
Author(s):  
Patrícia Franková ◽  
Martina Drahošová ◽  
Peter Balco
Keyword(s):  
Big Data ◽  
Project Management ◽  
Data Management ◽  
Management Approach ◽  
Agile Project Management

scholarly journals Design and analysis of a robust breast cancer diagnostic system based on multimode MR images

2021 ◽  
Vol 18 (4) ◽  
pp. 3578-3597
Author(s):  
Hong Yu ◽  
◽  
Wenhuan Lu ◽  
Qilong Sun ◽  
Haiqiang Shi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Diagnostic System ◽  
Mr Images ◽  
Cancer Diagnostic

scholarly journals Computer aided diagnosis for breast cancer based on the gabor filter technique

2020 ◽  
Vol 10 (5) ◽  
pp. 5235 ◽  
Author(s):  
Mohammed Y. Kamil
Keyword(s):  
Breast Cancer ◽  
Gabor Filter ◽  
Diagnostic System ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
Support Vector ◽  
Cancer Early Detection ◽  
False Positive Reduction ◽  
Computer Aided ◽  
Aided Diagnosis

The most prominent reason for the death of women all over the world is breast cancer. Early detection of cancer helps to lower the death rate. Mammography scans determine breast tumors in the first stage. As the mammograms have slight contrast, thus, it is a blur to the radiologist to recognize micro growths. A computer-aided diagnostic system is a powerful tool for understanding mammograms. Also, the specialist helps determine the presence of the breast lesion and distinguish between the normal area and the mass. In this paper, the Gabor filter is presented as a key step in building a diagnostic system. It is considered a sufficient method to extract the features. That helps us to avoid tumor classification difficulties and false-positive reduction. The linear support vector machine technique is used in this system for results classification. To improve the results, adaptive histogram equalization pre-processing procedure is employed. Mini-MIAS database utilized to evaluate this method. The highest accuracy, sensitivity, and specificity achieved are 98.7%, 98%, 99%, respectively, at the region of interest (30×30). The results have demonstrated the efficacy and accuracy of the proposed method of helping the radiologist on diagnosing breast cancer.

Sign in / Sign up

Export Citation Format

Share Document