Multi‐physics modeling to study the influence of tissue compression and cold stress on enhancing breast tumor detection using microwave radiometry

2019 ◽  
Vol 40 (4) ◽  
pp. 260-277 ◽  
Author(s):  
Rachana S. Akki ◽  
Sathya Priya Sugumar ◽  
Krishnamurthy C. Venkata ◽  
Kavitha Arunachalam
Keyword(s):  
Cold Stress ◽  
Breast Tumor ◽  
Tumor Detection ◽  
Microwave Radiometry ◽  
Physics Modeling ◽  
Tissue Compression

Numerical Simulation of Tunable Terahertz Graphene-Based Sensor for Breast Tumor Detection

2021 ◽  
Vol 21 (8) ◽  
pp. 9844-9851
Author(s):  
Aymen Hlali ◽  
Afef Oueslati ◽  
Hassen Zairi
Keyword(s):  
Numerical Simulation ◽  
Breast Tumor ◽  
Tumor Detection

3-D-Microwave Breast Tumor Detection: Study of System Performance

2008 ◽  
Vol 55 (12) ◽  
pp. 2772-2777 ◽  
Author(s):  
M.E. de Rodriguez ◽  
M. Vera-Isasa ◽  
V.S. del Rio
Keyword(s):  
Breast Tumor ◽  
System Performance ◽  
Tumor Detection

scholarly journals S-band five-port ring reflectometer-probe system for in vitro breast tumor detection

2017 ◽  
Vol 28 (3) ◽  
pp. e21198 ◽  
Author(s):  
Chia Yew Lee ◽  
Kok Yeow You ◽  
Zulkifly Abbas ◽  
Kim Yee Lee ◽  
Yeng Seng Lee ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Tumor Detection ◽  
Probe System

Breast Tumor Detection in MR Images Based on Density

2021 ◽  
pp. 2250001
Author(s):  
Neeraj Shrivastava ◽  
Jyoti Bharti
Keyword(s):  
Breast Cancer ◽  
Breast Tumor ◽  
Early Stage ◽  
Region Growing ◽  
Tumor Detection ◽  
Misclassification Rate ◽  
Seed Region ◽  
True Negative ◽  
Multiple Tumor ◽  
Breast Image

Breast cancer is dangerous in women. It is generally found after the symptoms appear. Detecting the breast cancer at an early stage and understanding the treatment are the most important strategies to prevent death from cancer. Generally, for detection of breast cancer, breast Magnetic Resonance Image (MRI) takes place. It is one of the best approaches to detect tumor in women. In this research paper, a combination of selection methods for seed region growing image segmentation is suggested to detect breast tumor. The suggested method has been divided into following parts: First, the pre-processing of breast image is performed. Second, the automatic threshold for binarization process is calculated. Third, the number of seed points and its position in the breast image are determined automatically using density of pixels value. Fourth, a method for calculation of threshold value is proposed for the purpose of region creation in seed region growing. For the evaluation purpose, the proposed method was applied and tested on the RIDER MRI breast dataset from National Biomedical Imaging Archive (NBIA). After the test was performed, it was observed that proposed algorithm gives 90% accuracy, 88% True Negative Fraction, 91% True Positive Fraction, 10% Misclassification Rate, 94% Precision and 86% Relative Overlap which is better than other existing methods. It not only gives better evaluation measure but also provides segmentation method for multiple tumor detection.

scholarly journals Near Field Breast Tumor Detection Using Ultra-Narrow Band Probe with Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Maged A. Aldhaeebi ◽  
Thamer S. Almoneef ◽  
Abdulbaset Ali ◽  
Zhao Ren ◽  
Omar M. Ramahi
Keyword(s):  
Machine Learning ◽  
Breast Tumor ◽  
Narrow Band ◽  
Near Field ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
Learning Techniques

A compact slotted patch antenna for breast tumor detection

2018 ◽  
Vol 60 (7) ◽  
pp. 1600-1608 ◽  
Author(s):  
M. Tarikul Islam ◽  
M. Samsuzzaman ◽  
M. N. Rahman ◽  
M. T. Islam
Keyword(s):  
Patch Antenna ◽  
Breast Tumor ◽  
Tumor Detection
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