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A quantifier-based fuzzy classification system for breast cancer patients
2013
Artificial Intelligence in Medicine
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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
2016
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
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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
2010
ECMS 2010 Proceedings edited by A Bargiela S A Ali D Crowley E J H Kerckhoffs
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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
2016
2016 IEEE Congress on Evolutionary Computation (CEC)
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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
2018
International Journal of Intelligent Systems and Applications
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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
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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
2012
International Journal of Information Technology Control and Automation
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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
2009
Pattern Recognition
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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
2017
IRBM
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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
2012
Journal of Applied Logic
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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
2016
International Journal of Computer Applications
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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
2015
BMC Medical Genomics
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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
2015
International Journal of Fuzzy Systems
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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
2012
Zenodo
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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
2015
Academic Radiology
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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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