THE OPPORTUNITY OF MAGNETIC INDUCTION TOMOGRAPHY MODALITY IN BREAST CANCER DETECTION

2016 ◽  
Vol 78 (7-4) ◽  
Author(s):  
Gowry Balasena ◽  
Lynn Sim ◽  
Zulkarnay Zakaria ◽  
Shahriman Abu Bakar ◽  
Mohamad Aliff Abd Rahim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Magnetic Induction ◽  
Breast Cancer Detection ◽  
Invasive Technique ◽  
Main Concern ◽  
Medical Field ◽  
Non Invasive ◽  
The World ◽  
Magnetic Induction Tomography

The needs for non-invasive technique in breast cancer detection could enhance and preserve the future of medical field in Malaysia as well as countries around the world. Breast cancer has become the main concern nowadays not only for women but for man as well. In overall, the risk of women getting breast cancer is higher than man due to the denser tissue of breast in women compare to man. Beside the unawareness for the disease, the reason which contributes to this increasing number of breast cancer reported is also due to the limitations arising from modalities such as MRI, Mammography, ultrasound and other modalities. An alternative to current technologies should be improved for early detection and treatment which causes no physical harm to patients if possible. Thus, non-invasive and better technology in detecting breast cancer is very much needed in the current market. This paper will be discussing the insights of Magnetic Induction Tomography techniques in breast cancer detection.

Urinary Exosomal MicroRNAs as Potential Non-invasive Biomarkers in Breast Cancer Detection

2020 ◽  
Vol 24 (2) ◽  
pp. 215-232 ◽  
Author(s):  
Marc Hirschfeld ◽  
Gerta Rücker ◽  
Daniela Weiß ◽  
Kai Berner ◽  
Andrea Ritter ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Non Invasive

Urinary exosomal microRNAs as potential non-invasive biomarkers in breast cancer detection

2020 ◽  
Author(s):  
K Berner ◽  
M. Hirschfeld ◽  
G Rücker ◽  
D Weiß ◽  
A Ritter ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Non Invasive

scholarly journals Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 551 ◽  
Author(s):  
Fayez AlFayez ◽  
Mohamed W. Abo El-Soud ◽  
Tarek Gaber
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Cancer Detection ◽  
Survival Rates ◽  
Histogram Equalization ◽  
Breast Cancer Detection ◽  
Machine Learning Techniques ◽  
World Health ◽  
The World ◽  
Health Organization

Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%.

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