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%.

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.

scholarly journals HUBUNGAN PENGETAHUAN DAN SIKAP DENGAN TINDAKAN PERSONAL HYGIENE SAATMENSTRUASI PADA REMAJA PUTRI

2018 ◽  
Vol 9 (2) ◽  
pp. 165-170
Author(s):  
Husni Husni
Keyword(s):  
Breast Cancer ◽  
Cervical Cancer ◽  
Random Sampling ◽  
World Health ◽  
Personal Hygiene ◽  
Bivariate Analysis ◽  
Stratified Random Sampling ◽  
The World ◽  
Health Organization

One of the effects is not maintaining hygiene during menstruation is able to hitkankes Rahim neck (cervical). Based on data from the World Health Organization (WHO),cervical cancer is the second most cancer in women aged 15-45 years after breast cancer. Noless than 500,000 new cases with 280,000 patient deaths occur each year worldwide.Indonesia was ranked first by the victims died at least 555 women per day and 200,000women annually. This study aims to determine the correlation between knowledge andattitude towards personal hygiene during menstruation action at SMAN 2 Bengkulu City. Thisresearch is descriptive analytic. The number of respondents 84 people with a samplingtechnique that stratified random sampling. Presentation of data is done by using a frequencydistribution table. The collection of data taken using a questionnaire. The data were analyzedusing univariate and bivariate analysis with Chi-Square.The results showed that therespondents are classified as good knowledge of (54.8%), attitude unfavorabel or does notsupport (53.6%), and the biggest acts (52.4%) is good. From the bivariate analysis were foundno correlation between knowledge against acts of personal hygiene during menstruation (p =0.794), and no relation attitude towards personal hygiene actions during menstruation (p =0.975).Required role of schools, educators, parents to be more proactive in enhancingknowledge and useful information about the process of menstruation and how to maintainhygiene during menstruation.

Potential Use of Biomarkers to Augment Clinical Decisions for the Early Detection of Breast Cancer

2014 ◽  
Vol 10 (02) ◽  
pp. 103 ◽  
Author(s):  
Alan B Hollingsworth ◽  
David E Reese ◽  
◽  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Survival Rates ◽  
High Sensitivity ◽  
Standard Of Care ◽  
Serum Biomarkers ◽  
Breast Cancer Detection ◽  
Breast Cancers

Breast cancer remains a significant worldwide health problem, despite the fact that early detection is associated with excellent survival rates. Currently, a substantial proportion of breast cancers are not detected using routine screening. Therefore, there is a need to identify a technology that can improve the precision and accuracy of early breast cancer detection. Biomarkers are attractive in that they can potentially detect early cancers with high sensitivity, while distinguishing between benign disease and invasive cancers. Many commonly used serum biomarkers have limited use in screening assays for breast cancer as single agents due to the heterogeneous nature of breast cancer. However, the use of protein panels that detect multiple serum biomarkers offer the potential for enhanced sensitivity and specificity in a clinical setting. Recently, a serum biomarker test comprising five serum biomarkers for breast cancer was clinically validated and showed high sensitivity and specificity. Additional panels have been developed that combine serum protein biomarkers (SPB) and tumor-associated autoantibodies (TAb) to further enhance the clinical utility of the assay. Serum biomarkers are currently not the standard of care and are not recommended in any detection guidelines. However, tumor biomarkers are used in the breast cancer setting to determine the course of care. The purpose of this article is to review recent advances in SPB, TAb, and biomarkers used in breast cancer detection to provide a perspective on how these technologies may offer benefit when combined with current imaging modalities.

scholarly journals Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

2018 ◽  
Author(s):  
Sandip S Panesar ◽  
Rhett N D’Souza ◽  
Fang-Cheng Yeh ◽  
Juan C Fernandez-Miranda
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Molecular Characteristics ◽  
Regression Methods ◽  
Learning Techniques ◽  
The World ◽  
Health Organization

AbstractBackgroundMachine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathological features. Recently the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly-dimensional database of glioma patients.MethodsWe applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients of all grades. We compared the effect of applying the algorithms to the raw database, versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).ResultsRaw input consisted of 21 variables, and achieved performance of (accuracy/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 for ANN, 73.3%/0.74 for SVM, 69.3%/0.73 for LR and 65.2%/0.63 for DT.ConclusionsWe demonstrate that these techniques can also be applied to small, yet highly-dimensional datasets. Our ML techniques achieved reasonable performance compared to similar studies in the literature. Though local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis, though traditional statistical methods are of similar benefit.

scholarly journals Understanding breast cancer as a global health concern

2021 ◽  
Author(s):  
Louise Wilkinson ◽  
Toral Gathani
Keyword(s):  
Breast Cancer ◽  
Global Health ◽  
Survival Rates ◽  
Economic Transformation ◽  
World Health ◽  
Health Concern ◽  
Comprehensive Treatment ◽  
The World ◽  
Delays In Diagnosis ◽  
And Control

Breast cancer is now the most commonly diagnosed cancer in the world. The most recent global cancer burden figures estimate that there were 2.26 million incident breast cancer cases in 2020 and the disease is the leading cause of cancer mortality in women worldwide. The incidence is strongly correlated with human development, with a large rise in cases anticipated in regions of the world that are currently undergoing economic transformation. Survival, however, is far less favourable in less developed regions. There are a multitude of factors behind disparities in the global survival rates, including delays in diagnosis and lack of access to effective treatment. The World Health Organization’s new Global Breast Cancer Initiative was launched this year to address this urgent global health challenge. It aims to improve survival across the world through three pillars: health promotion, timely diagnosis, and comprehensive treatment and supportive care. In this article, we discuss the key challenges of breast cancer care and control in a global context.

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