Using curvelet transform to detect breast cancer in digital mammogram

2009 ◽  
Author(s):  
Mohamed Meselhy M. Eltoukhy ◽  
Ibrahima Faye ◽  
Brahim Belhaouari Samir
Keyword(s):  
Breast Cancer ◽  
Curvelet Transform ◽  
Digital Mammogram ◽  
Detect Breast Cancer

scholarly journals Breast Cancer Detection from a Urine Sample by Dog Sniffing: A Preliminary Study for the Development of a New Screening Device and a Literature Review

Biology ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 517
Author(s):  
Shoko Kure ◽  
Shinya Iida ◽  
Marina Yamada ◽  
Hiroyuki Takei ◽  
Naoyuki Yamashita ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Cancer Patients ◽  
Urine Sample ◽  
Urine Samples ◽  
Breast Cancer Patients ◽  
Double Blind ◽  
Blind Test ◽  
Preliminary Study ◽  
Detect Breast Cancer

Background: Breast cancer is a leading cause of cancer death worldwide. Several studies have demonstrated that dogs can sniff and detect cancer in the breath or urine sample of a patient. This study aims to assess whether the urine sample can be used for breast cancer screening by its fingerprints of volatile organic compounds using a single trained sniffer dog. This is a preliminary study for developing the “electronic nose” for cancer screening. Methods: A nine-year-old female Labrador Retriever was trained to identify cancer from urine samples of breast cancer patients. Urine samples from patients histologically diagnosed with primary breast cancer, those with non-breast malignant diseases, and healthy volunteers were obtained, and a double-blind test was performed. Total of 40 patients with breast cancer, 142 patients with non-breast malignant diseases, and 18 healthy volunteers were enrolled, and their urine samples were collected. Results: In 40 times out of 40 runs of a double-blind test, the trained dog could correctly identify urine samples of breast cancer patients. Sensitivity and specificity of this breast cancer detection method using dog sniffing were both 100%. Conclusions: The trained dog in this study could accurately detect breast cancer from urine samples of breast cancer patients. These results indicate the feasibility of a method to detect breast cancer from urine samples using dog sniffing in the diagnosis of breast cancer. Although the methodological standardization is still an issue to be discussed, the current result warrants further study for developing a new breast cancer screening method based on volatile organic compounds in urine samples.

scholarly journals Nipple aspirate fluid and ductoscopy to detect breast cancer

2009 ◽  
pp. NA-NA ◽  
Author(s):  
Edward R. Sauter ◽  
Andres Klein-Szanto ◽  
Brenda MacGibbon ◽  
Hormoz Ehya,
Keyword(s):  
Breast Cancer ◽  
Nipple Aspirate Fluid ◽  
Detect Breast Cancer

Digital Mammograms with Image Enhancement Techniques for Breast Cancer Detection: A Systematic Review

2021 ◽  
Vol 17 ◽  
Author(s):  
Saifullah Harith Suradi ◽  
Kamarul Amin Abdullah
Keyword(s):  
Breast Cancer ◽  
Image Quality ◽  
Image Enhancement ◽  
Cancer Detection ◽  
Mean Squared Error ◽  
Survival Rates ◽  
Curvelet Transform ◽  
Breast Cancer Detection ◽  
Online Databases ◽  
Fast Discrete Curvelet Transform

Background: Digital mammograms with appropriate image enhancement techniques will improve breast cancer detection, and thus increase the survival rates. The objectives of this study were to systematically review and compare various image enhancement techniques in digital mammograms for breast cancer detection. Methods: A literature search was conducted with the use of three online databases namely, Web of Science, Scopus, and ScienceDirect. Developed keywords strategy was used to include only the relevant articles. A Population Intervention Comparison Outcomes (PICO) strategy was used to develop the inclusion and exclusion criteria. Image quality was analyzed quantitatively based on peak signal-noise-ratio (PSNR), Mean Squared Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy, and Contrast Improvement Index (CII) values. Results: Nine studies with four types of image enhancement techniques were included in this study. Two studies used histogram-based, three studies used frequency-based, one study used fuzzy-based and three studies used filter-based. All studies reported PSNR values whilst only four studies reported MSE, AMBE, Entropy and CII values. Filter-based was the highest PSNR values of 78.93, among other types. For MSE, AMBE, Entropy, and CII values, the highest were frequency-based (7.79), fuzzy-based (93.76), filter-based (7.92), and frequency-based (6.54) respectively. Conclusion: In summary, image quality for each image enhancement technique is varied, especially for breast cancer detection. In this study, the frequency-based of Fast Discrete Curvelet Transform (FDCT) via the UnequiSpaced Fast Fourier Transform (USFFT) shows the most superior among other image enhancement techniques.

A Survey on Early Detection of Women's Breast Cancer Using IoT

2021 ◽  
pp. 208-219
Author(s):  
P. Malathi ◽  
A. Kalaivani
Keyword(s):  
Breast Cancer ◽  
Internet Of Things ◽  
Early Detection ◽  
Time Data ◽  
Diagnose Breast Cancer ◽  
Current State ◽  
Past Data ◽  
Self Examination ◽  
The Internet Of Things ◽  
Detect Breast Cancer

The internet of things is probably one of the most challenging and disruptive concepts raised in recent years. Recent development in innovation and availability have prompted the rise of internet of things (IoT). IoT technology is used in a wide scope of certified application circumstances. Internet of things has witnessed the transition in life for the last few years which provides a way to analyze both the real-time data and past data by the emerging role. The current state-of-the-art method does not effectively diagnose breast cancer in the early stages. Thus, the early detection of breast cancer poses a great challenge for medical experts and researchers. This chapter alleviates this by developing a novel software to detect breast cancer at a much earlier stage than traditional methods or self-examination.

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