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A quantifier-based fuzzy classification system for breast cancer patients

Daniele Soria, Jonathan M. Garibaldi, Andrew R. Green, Desmond G. Powe, Christopher C. Nolan, Christophe Lemetre, Graham R. Ball, Ian O. Ellis
2013 Artificial Intelligence in Medicine  
O. (2013) A quantifier-based fuzzy classification system for breast cancer patients.  ...  Methods and materials: In this paper, we extend a data-driven fuzzy rule-based system for classification purposes (called 'fuzzy quantification subsethood-based algorithm') and combine it with a novel  ...  A quantifier based on fuzzy sets seems to be more suitable for quantifier based fuzzy models which are described in natural language.  ... 
doi:10.1016/j.artmed.2013.04.006 pmid:23791088 fatcat:az6ezsryn5fbhjhu6lcw7msd4e

Validation of a Quantifier-Based Fuzzy Classification System for Breast Cancer Patients on External Independent Cohorts

Daniele Soria, Jonathan M. Garibaldi
2016 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)  
The objective of this study is to present the validation of a recently developed extension of a fuzzy quantification subsethood-based algorithm on three sets of newly available breast cancer data.  ...  It is known that fuzzy methodologies can provide linguistic based classification rules to ease those from consensus clustering.  ...  The fuzzyQSBA algorithm uses fuzzy subsethood measures, rule induction approaches, and fuzzy quantifiers to produce a list of linguistic rules which can then be used for classification purposes.  ... 
doi:10.1109/icmla.2016.0101 dblp:conf/icmla/SoriaG16 fatcat:savznkq76zfirgrbamqp6c6pw4

Consensus Clustering And Fuzzy Classification For Breast Cancer Prognosis

Jonathan M. Garibaldi, Daniele Soria, Khairul A. Rasman
2010 ECMS 2010 Proceedings edited by A Bargiela S A Ali D Crowley E J H Kerckhoffs  
We then use fuzzy rule induction and simplification algorithms to generate a simple, comprehensible set of rules for use in future model-based classification.  ...  We first use a consensus clustering methodology to identify core, well-characterised sub-groups (or classes) of the disease based on a large database of protein biomarkers from over a thousand patients  ...  In future, we aim to implement the resultant fuzzy rule table in a model-based classification system that can be used to determine the type (class) of cancer in new patients presenting with breast cancer  ... 
doi:10.7148/2010-0015-0022 dblp:conf/ecms/GaribaldiSR10 fatcat:wmv4jvb6abbg5j33kiarpa5yae

Cancer subtype identification pipeline: A classifusion approach

Utkarsh Agrawal, Daniele Soria, Christian Wagner
2016 2016 IEEE Congress on Evolutionary Computation (CEC)  
Classification of cancer patients into treatment groups is essential for appropriate diagnosis to increase survival.  ...  Finally, we present a small set of recent findings on the Nottingham Tenovus Primary Breast Carcinoma Series enabling the classification of a higher number of patients into one of the identified breast  ...  The work makes use of a data-driven fuzzy rule based system (FuzzyQSBA) for classifying the breast cancer patients.  ... 
doi:10.1109/cec.2016.7744150 dblp:conf/cec/AgrawalSW16 fatcat:tz6osx5jnrhongla4cenjmfjdy

Extracting a Linguistic Summary from a Medical Database

Djazia AMGHAR, Department of Computer Science, Biomedical Engineering Laboratory, University Abou Bekr Belkaid – Tlemcen, B.P.230- Tlemcen 13000, Algérie, Amine.M. CHIKH
2018 International Journal of Intelligent Systems and Applications  
Pima Indians Diabetes dataset and Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The results obtained were then employed for a benchmark test.  ...  The present study was successful in developing a classification system that is based on the linguistic summary of two datasets from the UCI Machine Learning Repository, i.e.  ...  The breast cancer database contains medical information of 699 clinical cases having breast cancer and classified as malignant or benignant; 458 patients (65.5%) were mild cases and 241 patients (34.5%  ... 
doi:10.5815/ijisa.2018.12.02 fatcat:3fs4elcvdbeonbyfglzlumeeiy

A Fuzzy Rule-based Expert System for the Prognosis of the Risk of Development of the Breast Cancer

2014 International Journal of Engineering  
This research presents a fuzzy expert system for breast cancer prognosis. This approach is capable enough to capture ambiguity and imprecision prevalent in the characterization of the breast cancer.  ...  A fuzzy expert system models knowledge as a set of explicit linguistic rules and performs reasoning with words.  ...  This paper presents a fuzzy expert system for the breast cancer prognosis.  ... 
doi:10.5829/idosi.ije.2014.27.10a.09 fatcat:jx4y2ryy5fbnzagltjsqaskb7u

Computer Aided System for Detection and Classification of Breast Cancer

S Shanthi
2012 International Journal of Information Technology Control and Automation  
This paper proposes a computer aided system for automatic detection and classification of breast cancer in mammogram images.  ...  Breast cancer is one of the most important causes of death among all type of cancers for grown-up and older women, mainly in developed countries, and its rate is rising.  ...  Till now there is no known way to prevent breast cancer but the earlier the cancer is detected, the higher the chance of survival for patients.  ... 
doi:10.5121/ijitca.2012.2408 fatcat:kpyfnohvpbhi3mvexseine25le

Thermography based breast cancer analysis using statistical features and fuzzy classification

Gerald Schaefer, Michal Závišek, Tomoharu Nakashima
2009 Pattern Recognition  
In this paper we perform breast cancer analysis based on thermography, using a series of statistical features extracted from the thermograms coupled with a fuzzy rule-based classification system for diagnosis  ...  These features are then fed into a fuzzy if-then rule based classification system which outputs a diagnostic prediction of the investigated patient.  ... 
doi:10.1016/j.patcog.2008.08.007 fatcat:ct5lignjxngsvezeuep5nshmiy

Fibroglandular Tissue Quantification in Mammography by Optimized Fuzzy C-Means with Variable Compactness

A.L.M. Pavan, A. Vacavant, A.P. Trindade, D.R. de Pina
2017 IRBM  
Mammography is a wordwild image modality used to diagnose breast cancer, even for asymptomatic women.  ...  Our automatic approach utilizes an optimized Fuzzy C-Means with variable compactness algorithm to classify and quantify fibroglandular tissue in mammograms.  ...  In clinical routine, radiologists perform subjective visual assessments based on Breast Imaging Reporting and Data Systems (BI-RADS) density classification [2, 3] .  ... 
doi:10.1016/j.irbm.2017.05.002 fatcat:aqudqgi2czfpxhyyaa6dz3i2nm

Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural networks

Aboul Ella Hassanien, Tai-hoon Kim
2012 Journal of Applied Logic  
Finally, a support vector machine classifier was employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether they represent cancer  ...  This article introduces a hybrid approach that combines the advantages of fuzzy sets, pulse coupled neural networks (PCNNs), and support vector machine, in conjunction with wavelet-based feature extraction  ...  Acknowledgements This work has been supported by Cairo University, project Bio-inspired Technology in Women Breast Cancer Classification, Prediction and Visualization.  ... 
doi:10.1016/j.jal.2012.07.003 fatcat:yr75twjffrbktf7db464h5swaa

Developing of Fuzzy Logic Decision Support for Management of Breast Cancer

Sameh Mohamed, Wael Mohamed
2016 International Journal of Computer Applications  
This paper aims to describe an intelligent procedure based on fuzzy logic techniques and medical model to detect and diagnose Breast.  ...  Automatic diagnosis of breast cancer is an important, that's really real-world medical problem.  ...  expert oncologist, the cancerous development stage of the detected lesion A fuzzy logic technique for the prediction of the risk of breast cancer based on a set of judiciously chosen fuzzy rules utilizing  ... 
doi:10.5120/ijca2016910585 fatcat:kalnxrlxmjdmrhrdo32srait7e

Fuzzy logic selection as a new reliable tool to identify molecular grade signatures in breast cancer – the INNODIAG study

Tatiana Kempowsky-Hamon, Carine Valle, Magali Lacroix-Triki, Lyamine Hedjazi, Lidwine Trouilh, Sophie Lamarre, Delphine Labourdette, Laurence Roger, Loubna Mhamdi, Florence Dalenc, Thomas Filleron, Gilles Favre (+3 others)
2015 BMC Medical Genomics  
The aims of this study were to develop a new gene selection method based on a fuzzy logic selection and classification algorithm, and to validate the gene signatures obtained on breast cancer patient cohorts  ...  Personalized medicine has become a priority in breast cancer patient management.  ...  We thank the ethics committee "Claudius Regaud Cancer Institute Research committee" that approved our study and gave us access to the biological resources and patient database.  ... 
doi:10.1186/s12920-015-0077-1 pmid:25888889 pmcid:PMC4342216 fatcat:5zrj2vaugngyjjuz2xh546ziaq

Kernel-based Fuzzy-rough Nearest-neighbour Classification for Mammographic Risk Analysis

Yanpeng Qu, Changjing Shang, Qiang Shen, Neil Mac Parthaláin, Wei Wu
2015 International Journal of Fuzzy Systems  
1 Mammographic risk analysis is an important task for assessing the likelihood of a woman developing breast cancer.  ...  This demonstrates the potential of kernel-based fuzzy-rough nearest-neighbour classification as a robust and reliable tool for mammographic risk analysis.  ...  Conclusions In this paper, an effective classification approach, kernel-based fuzzy-rough nearest-neighbour (KFRNN) and a direct extension of it, kernel-based vaguely quantified nearest-neighbour (KVQNN  ... 
doi:10.1007/s40815-015-0044-1 fatcat:4lfkzdevwbepdejngqs6etwmam

An Overview Of The Application Of Fuzzy Inference System For The Automation Of Breast Cancer Grading With Spectral Data

Shabbar Naqvi, Jonathan M. Garibaldi
2012 Zenodo  
In this paper we present an overview of the use of advanced computational method of fuzzy inference system as a tool for the automation of breast cancer grading.  ...  The future work outlines the potential areas of fuzzy systems that can be used for the automation of breast cancer grading.  ...  systems that can be used for cancer spectral data sets in the area of breast cancer for the automation of breast cancer grading.  ... 
doi:10.5281/zenodo.1083531 fatcat:6vlkxzamwjbo7kexgd3eu75umq

Breast Density Evaluation Using Spectral Mammography, Radiologist Reader Assessment, and Segmentation Techniques

Sabee Molloi, Huanjun Ding, Stephen Feig
2015 Academic Radiology  
Materials and Methods-Spectral mammography images from a total of 92 consecutive asymptomatic women (50-69 years old) who presented for annual screening mammography were retrospectively analyzed for this  ...  The relative standard error of estimate for breast density measurements from left and right breasts using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm and dual-energy  ...  risk for breast cancer (19) .  ... 
doi:10.1016/j.acra.2015.03.017 pmid:26031229 pmcid:PMC4515382 fatcat:j2lcp3i63vhr3plhv7nrajeyki
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