Breast cancer

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Breast cancer is cancer originating from breast tissue, most commonly from the inner lining of milk ducts or the lobules that supply the ducts with milk. (Wikipedia.org)






Conferences related to Breast cancer

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2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting

The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science


2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Robotics and Automation (ICRA)

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.


2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

All areas of ionizing radiation detection - detectors, signal processing, analysis of results, PET development, PET results, medical imaging using ionizing radiation


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Periodicals related to Breast cancer

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Biomedical Engineering, IEEE Reviews in

The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


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Xplore Articles related to Breast cancer

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Classification Bias of the k-Nearest Neighbor Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984

The k-nearest neighbor classifier has been used extensively in pattern analysis applications. This classifier can, however, have substantial bias when there is little class separation and the sample sizes are unequal. This classification bias is examined for the two-class situation and formulas presented that allows selection of values of k that yields minimum bias.


Contrast enhancement mammograms using denoising in wavelet coefficients

The 2013 10th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2013

Contrast enhancement in x-ray mammograms is important in improvement of radiologists' reading and interpretation. In many cases, it is difficult to discern signs of breast cancer because mammograms are low contrast and very noisy. This paper proposes improved contrast enhancement method in mammograms using denoising in wavelet coefficients. First, mammograms are decomposed by wavelet transform. Second, the ratio between approximate ...


Detector module of a single photon compact ring tomograph for high resolution breast imaging

Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143), 2000

The initial characterization results of detector modules allow one to foresee that the single photon compact ring tomograph for high resolution breast imaging can be a promising technique as well as having the exciting potential to make a real contribution to the field of breast imaging. This project is a part of larger research project including simultaneous transmission and emission ...


Design of a UWB 6-port reflectometer formed by microstrip-slot couplers for use in a microwave breast cancer detection system

2007 IEEE Antennas and Propagation Society International Symposium, 2007

The design of an ultra wideband (UWB) reflectometer for use in a microwave breast cancer detection system is presented. The design is based on a six-port technique. The reflectometer is constructed using an assembly of compact UWB microstrip-slot couplers. Its analysis and performance assessment are accomplished using commercially available circuit and EM field simulation tools.


Novel image processing techniques for early detection of breast cancer, mat lab and lab view implementation

2013 IEEE Point-of-Care Healthcare Technologies (PHT), 2013

Early detection of breast cancer is carried out by using mammographic images. Due to low contrast nature of these images, it is difficult to detect signs such as microcalcifications and masses. This paper describes novel algorithms for early detection of breast cancer using image enhancement techniques to identify masses and microcalcifications. We implemented algorithm for 1) Image enhancement using wavelets ...


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Educational Resources on Breast cancer

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IEEE-USA E-Books

  • Classification Bias of the k-Nearest Neighbor Algorithm

    The k-nearest neighbor classifier has been used extensively in pattern analysis applications. This classifier can, however, have substantial bias when there is little class separation and the sample sizes are unequal. This classification bias is examined for the two-class situation and formulas presented that allows selection of values of k that yields minimum bias.

  • Contrast enhancement mammograms using denoising in wavelet coefficients

    Contrast enhancement in x-ray mammograms is important in improvement of radiologists' reading and interpretation. In many cases, it is difficult to discern signs of breast cancer because mammograms are low contrast and very noisy. This paper proposes improved contrast enhancement method in mammograms using denoising in wavelet coefficients. First, mammograms are decomposed by wavelet transform. Second, the ratio between approximate subband and detail subband as signal-to-noise ratio is computed. Finally, detail subbands which have the ratio lower than the criteria are boosted to improve mammograms contrast while detail subbands having the ratio higher than the criteria are set to zero for noise suppression in the same time. Contrast measure and peak- signalto-noise ratio are used to evaluate the performance of the proposed method. The experimental results show higher contrast 17.05% than the conventional method while a bit different in peak-signal-to-noise ratio.

  • Detector module of a single photon compact ring tomograph for high resolution breast imaging

    The initial characterization results of detector modules allow one to foresee that the single photon compact ring tomograph for high resolution breast imaging can be a promising technique as well as having the exciting potential to make a real contribution to the field of breast imaging. This project is a part of larger research project including simultaneous transmission and emission tomography. Finally, this tomographic system can have a relevance which extends beyond the field of breast imaging to other areas of nuclear medicine imaging as well as the more general scope of nuclear radiation detection.

  • Design of a UWB 6-port reflectometer formed by microstrip-slot couplers for use in a microwave breast cancer detection system

    The design of an ultra wideband (UWB) reflectometer for use in a microwave breast cancer detection system is presented. The design is based on a six-port technique. The reflectometer is constructed using an assembly of compact UWB microstrip-slot couplers. Its analysis and performance assessment are accomplished using commercially available circuit and EM field simulation tools.

  • Novel image processing techniques for early detection of breast cancer, mat lab and lab view implementation

    Early detection of breast cancer is carried out by using mammographic images. Due to low contrast nature of these images, it is difficult to detect signs such as microcalcifications and masses. This paper describes novel algorithms for early detection of breast cancer using image enhancement techniques to identify masses and microcalcifications. We implemented algorithm for 1) Image enhancement using wavelets and adaptive histogram equalization technique 2) Segmentation of masses is done using region growing technique 3) Extraction of border of the mass using canny edge detection and morphological operations. Bilateral asymmetry was detected using fluctuating asymmetry [9]. The paper presents case studies of four patients, though fourteen patient breast images are processed having different mammographic features.

  • Tsallis Entropy Based Contrast Enhancement of Microcalcifications

    This paper presents a new approach to enhance the contrast of microcalcifications in mammograms using a fuzzy algorithm based on Tsallis entropy. In phase I image is fuzzified using S membership function. In Phase II using the non-uniformity factor calculated from local information the contrast of microcalcifications were enhanced while suppressing the background heavily. This is the first time in literature to propose an enhancement algorithm using Tsallis entropy. Tsallis entropy has an extra parameter q. We assume that grade of mammogram is related with q parameter. The values of q were calculated from the histogram. The proposed approach can be even suitable for dense mammograms.

  • Self-regulated multilayer perceptron neural network for breast cancer classification

    The algorithm named self-regulated multilayer perceptron neural network for breast cancer classification (ML-NN) is designed for breast cancer classification. Conventionally, medical doctors need to manually delineate the suspicious breast cancer region. Many studies have suggested that segmentation manually is not only time consuming, but also machine and operator dependent. ML-NN utilise multilayer perceptron neural network on breast cancer classification to aid medical experts in diagnosis of breast cancer. Trained ML-NN can categorise the input medical images into benign, malignant and normal patients. By applying the present algorithm, breast medical images can be classified into cancer patient and normal patient without prior knowledge regarding the presence of cancer lesion. This method is aimed to assist medical experts for breast cancer patient diagnosis through implementation of supervised Multilayer Perceptron Neural Network. ML-NN can classified the input medical images as benign, malignant or normal patient with accuracy, specificity, sensitivity and AUC of 90.59%, 90.67%, 90.53%, and 0.906 ± 0.0227 respectively.

  • A Combining Method for Tumors Detection from Near-infrared Breast Imaging

    This paper introduces the new qualitative and quantitative methods, which can diagnose breast tumors. Qualitative methods include blood vessel display inside and outside of pathological changes part of breast, display of equivalent pixel curves at the part of pathological changes and display of breast tumor image edge. Accordingly, three feature extraction operators are proposed, i.e. the combination operators of anisotropic gradient and smoothing operator, an improved Sobel operator and an edge sharpening operator. Furthermore, quantitative diagnostic approaches are discussed based on blood and oxygen contents according to abundant clinical data and pathological mechanism of breast tumors. The results of clinic show that the methods of combining qualitative and quantitative diagnose are effective for breast tumor images, especially for early and potential breast cancer

  • Classification of post operative breast cancer patient information using complex valued neural classifiers

    Classification of Haberman's Survival information is useful to find out the patients survival probability after a breast cancer surgery. Dataset has been collected from a standard benchmark UCI machine learning repository. A study at the hospital named University of Chicago's Billings was conducted between the year 1958 and 1970 to identify the cancer patients who had undergone surgery for breast cancer and survived. The data obtained are classified using a fully complex valued classifier in this paper. Classifying patient's survival after five years and patients death within five years is a challenging prognosis problem. The effectiveness of the classification achieved can be used by the clinicians for the treatment of patients in the hospitals. For achieving better discrimination, the proposed method uses a fully complex valued fast learning classifier with Gd activation function in the hidden layer. Comparing the classification efficiency of FC-FLC with other networks available in the literature, FC-FLC provides a better classification performance than the SRAN, MCFIS and ELM classifier.

  • Not your mother's mammography [breast cancer detection]

    Breast cancer accounts for nearly one of every three cancers diagnosed in US women. While great strides have been made in early detection, the conventional method of mammography is not failproof it has trouble imaging dense tissue, it may show suspicious areas where no malignancy exists, and radiologists interpreting the images can miss up to 15 percent of cancers. It's also uncomfortable, requiring each breast to be compressed between plastic plates, which can lead to bruising. Susan Hagness wants to change all that. An assistant professor of electrical engineering at the University of Wisconsin- Madison, she is pioneering a novel detection technique that uses ultrawideband microwaves to image even the tiniest malignant tumors in the breast. Breast tumors and normal tissue show much more contrast at microwave frequencies than at the X-ray frequencies used for mammograms. Microwaves are also nonionizing, and the technique requires no breast compression. In Susan Hagness' search for a better way to detect breast cancer, she gets her students involved, too. Such efforts have paid off: her courses consistently receive high marks on student evaluations.



Standards related to Breast cancer

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