New Approaches To Classification And Diagnostic Prediction Of Breast Cancers
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New Approaches To Classification And Diagnostic Prediction Of Breast Cancers
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Author : Aleix Prat
language : en
Publisher: Frontiers Media SA
Release Date : 2020-06-16
New Approaches To Classification And Diagnostic Prediction Of Breast Cancers written by Aleix Prat and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-16 with categories.
Despite many years of translational research in breast cancer, very few new biomarkers have been implemented for clinical use beyond estrogen receptor, progesterone receptor, and HER2. The main reason is that many promising biomarkers are clinically validated but lack analytical and clinical utility. One explanation is that proper validation of the predictive ability of the biomarker in independent datasets, and with a pre-planned statistical analysis, is not always performed. Thus, there is a need to identify new biomarkers or new ways to subclassify breast cancer patients that are reproducible and easy to implement in the clinical setting but, more importantly, that improve patient’s outcomes.
New Approaches For
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Author : Wafaa Shousha
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2013
New Approaches For written by Wafaa Shousha and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Biomarkers accepted for clinical use in breast cancer, such as CA 15-3 and CEA have low sensitivity and specificity, and are thus more useful for patients at an advanced stage of breast cancer rather than for early cancer diagnosis. So, there is a need for new biomarkers to help in diagnosis of primary breast cancer and this is one of the aims of the present study. Once a patient has been diagnosed with breast cancer, there are several factors shown to be associated with survival. These factors are referred to as prognostic factors such as axillary lymph node status, tumor size, histological grade and hormone receptor expression. All these factors require tissue samples which is not practical for a screening regimen. So, measurement of the parameters in the present study was on the serum. Angiogenesis is a key factor in cancer development. It is initiated when there is a predominance of angiogenic factors that favour new vessel growth such as VEGF. Besides VEGF, there are several growth factors and molecules included in angiogenesis such as HGF, IL-18 and nitric oxide.
Ensemble Learning Approach For Classification Variance Reduction In Breast Cancer Diagnosis
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Author :
language : en
Publisher:
Release Date : 2015
Ensemble Learning Approach For Classification Variance Reduction In Breast Cancer Diagnosis written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Breast categories.
Breast Cancer Classification Using Machine Learning An Empirical Study
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Author : Akor Ugwu
language : en
Publisher: GRIN Verlag
Release Date : 2021-05-11
Breast Cancer Classification Using Machine Learning An Empirical Study written by Akor Ugwu and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-11 with Medical categories.
Diploma Thesis from the year 2020 in the subject Medicine - Diagnostics, grade: 3.55, , course: Computer Science, language: English, abstract: The study will classify breast cancers into foremost problems: (Benign tumor and Malignant tumor). A benign tumor is a most cancers does now not invade its surrounding tissue or spread around the host. A malignant tumor is another kind of cancers which can invade its surrounding tissue or spread around the frame of the host. Benign cancers on uncommon event can also surely result in someone’s death, but as a fashionable rule they're no longer nearly as horrific because the malignant cancers. The malignant cancers at the contrary are like those killer bees. In this situation, you do not need to be doing something to them or maybe be everywhere near their hive, they will just spread out and attack you emass – they could even kill the individual if they are extreme enough. Manual manner of cancer category into benign and malignant may be very tedious, susceptible to human error and unnecessarily time consuming. The proposed system while constructed can robotically classify the sort of most cancers into the safe (benign) and also the risky (malignant). This machine plays this role through the usage of machine getting to know algorithm. The following is the extensive of this new system: Classification mistakes could be notably removed, early analysis of disorder, removal of possible human mistakes and the device does no longer die. However, the researcher seeks to detect and assess the class of breast using Machine learning.
Statistical Methods For Diagnostic Testing
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Author : Xin Sun
language : en
Publisher:
Release Date : 2013
Statistical Methods For Diagnostic Testing written by Xin Sun and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
This report illustrates how to use two statistic methods to investigate the performance of a new technique to detect breast cancer and lung cancer at early stages. The two methods include logistic regression and classification and regression tree (CART). It is found that the technique is effective in detecting breast cancer and lung cancer, with both sensitivity and specificity close to 0.9. But the ability of this technique to predict the actual stages of cancer is low. The age variable improves the ability of logistic regression in predicting the existence of breast cancer for the samples used in this report. But since the sample sizes are small, it is impossible to conclude that including the age variable helps the prediction of breast cancer. Including the age variable does not improve the ability to predict the existence of lung cancer. If the age variable is excluded, CART and logistic regression give a very close result.
An Efficient Classification Framework For Breast Cancer Using Hyper Parameter Tuned Random Decision Forest Classifier And Bayesian Optimization
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Author : Pratheep Kumar
language : en
Publisher: Infinite Study
Release Date :
An Efficient Classification Framework For Breast Cancer Using Hyper Parameter Tuned Random Decision Forest Classifier And Bayesian Optimization written by Pratheep Kumar and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Decision tree algorithm is one of the algorithm which is easily understandable and interpretable algorithm used in both training and application purpose during breast cancer prognosis. To address this problem, Random Decision Forests are proposed. In this manuscript, the breast cancer classification can be determined by combining the advantages of Feature Weight and Hyper Parameter Tuned Random Decision Forest classifier
Artificial Intelligence Applications And Innovations
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Author : Ilias Maglogiannis
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-18
Artificial Intelligence Applications And Innovations written by Ilias Maglogiannis and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-18 with Computers categories.
Artificial Intelligence applications build on a rich and proven theoretical background to provide solutions to a wide range of real life problems. The ever expanding abundance of information and computing power enables researchers and users to tackle higly interesting issues for the first time, such as applications providing personalized access and interactivity to multimodal information based on preferences and semantic concepts or human-machine interface systems utilizing information on the affective state of the user. The purpose of the 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI) is to bring together researchers, engineers, and practitioners interested in the technical advances and business and industrial applications of intelligent systems. AIAI 2006 is focused on providing insights on how AI can be implemented in real world applications.
Artificial Intelligence In Medicine
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Author : David Riaño
language : en
Publisher: Springer
Release Date : 2019-06-19
Artificial Intelligence In Medicine written by David Riaño and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-19 with Computers categories.
This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
Optimization In Machine Learning And Applications
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Author : Anand J. Kulkarni
language : en
Publisher: Springer Nature
Release Date : 2019-11-29
Optimization In Machine Learning And Applications written by Anand J. Kulkarni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-29 with Technology & Engineering categories.
This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.
Contrast Enhanced Digital Mammography Cedm
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Author : Jacopo Nori
language : en
Publisher: Springer
Release Date : 2019-02-11
Contrast Enhanced Digital Mammography Cedm written by Jacopo Nori and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-11 with Medical categories.
This book offers a comprehensive, practical resource entirely devoted to Contrast-Enhanced Digital Mammography (CEDM), a state-of-the-art technique that has emerged as a valuable addition to conventional imaging modalities in the detection of primary and recurrent breast cancer, and as an important preoperative staging tool for women with breast cancer. CEDM is a relatively new breast imaging technique based on dual energy acquisition, combining mammography with iodine-based contrast agents to display contrast uptake in breast lesions. It improves the sensitivity and specificity of breast cancer detection by providing higher foci to breast-gland contrast and better lesion delineation than digital mammography. Preliminary results suggest that CEDM is comparable to breast MRI for evaluating the extent and size of lesions and detecting multifocal lesions, and thus has the potential to become a readily available, fast and cost-effective examination. With a focus on the basic imaging principles of CEDM, this book takes a practical approach to breast imaging. Drawing on the editors’ and authors’ practical experience, it guides the reader through the basics of CEDM, making it especially accessible for beginners. By presenting the key aspects of CEDM in a straightforward manner and supported by clear images, the book represents a valuable guide for all practicing radiologists, in particular those who perform breast imaging and have recently incorporated or plan to incorporate CEDM into their diagnostic arsenal.