Feature Selection Based on Enhanced Cuckoo Search for Breast Cancer Classification in Mammogram Image

Circuits and Systems

ISSN Print: 2153-1285
ISSN Online: 2153-1293
www.scirp.org/journal/cs
E-mail: cs@scirp.org

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"Feature Selection Based on Enhanced Cuckoo Search for Breast Cancer Classification in Mammogram Image"
written by M. N. Sudha, S. Selvarajan,
published by Circuits and Systems, Vol.7 No.4, 2016

has been cited by the following article(s):

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[1]	An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection
	International Journal of System …, 2022

[2]	Gene subset selection using fuzzy statistical dependence technique and binary bat algorithm
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[3]	A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach
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[4]	An overview of Cuckoo Optimization Algorithm based Image Processing
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[5]	SEDDA-An Optimal Feature Selection Approach For Breast Cancer Diagnosis
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[6]	An Enhanced Binary Cuckoo Search Algorithm Using Crossover Operators for Features Selection
	2021 International Conference on …, 2021

[7]	Swarm intelligence algorithms in gene selection profile based on classification of microarray data: A review
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[8]	Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)
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[9]	Intelligent Feature Selection Methods: A Survey
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[10]	Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification
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[11]	ALOCAL DIRECTIONAL TERNARY PATTERN TEXTURE DESCRIPTOR FOR MAMMOGRAPHIC BREAST CANCER CLASSIFICATION
	2020

[12]	An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine
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[13]	Breast cancer diagnosis using a Minkowski distance method based on mutual information and genetic algorithm
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[14]	Gene Selection Using Hybrid Multi-Objective Cuckoo Search Algorithm With Evolutionary Operators for Cancer Microarray Data
	2020

[15]	Breast Cancer Detection via Mammographic Images: A Survey
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[16]	Feature selection using improved lion optimisation algorithm for breast cancer classification
	2019

[17]	Optimized Model for Cervical Cancer Detection Using Binary Cuckoo Search
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[18]	Differentiation of Homogenous and Speckled Objects in HEp-2 Specimen Images using Keypoint Features and an Adaptive Cuckoo Search Algorithm
	2019

[19]	Comparative Analysis of Feature Selection in Epilepsy Seizure Recognition Using Cuckoo, Gravitational Search and Bat Algorithm
	2018

[20]	Prediagnóstico de cáncer de mama utilizando metaheurísticas y clasificación de imágenes
	2018

[21]	Extracting tumor in MR brain and breast image with Kapur's entropy based Cuckoo Search Optimization and morphological reconstruction filters
	Biocybernetics and Biomedical Engineering, 2018

[22]	Feature Selection based on Hybrid Binary Cuckoo Search and Rough Set Theory in Classification for Nominal Datasets
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[23]	A survey on medical image denoising using optimisation technique and classification
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[24]	Modified Binary Cuckoo Search for Feature Selection: A Hybrid Filter-Wrapper Approach
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[25]	K-Nearest Neighbor Classification Method for Mammogram Images
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[26]	A MODIFIED-WEIGHTED-K-NEAREST NEIGHBOUR AND CUCKOO SEARCH HYBRID MODEL FOR BREAST CANCER CLASSIFICATION


[27]	ARTIFICIAL INTELEGENCE FOR DNA ANALYSIS TO PREDICT GENETIC DISEASES

[1]	An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection International Journal of System Assurance Engineering and Management, 2024 DOI:10.1007/s13198-021-01598-7

[2]	Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions EAI/Springer Innovations in Communication and Computing, 2023 DOI:10.1007/978-3-031-15542-0_14

[3]	Smart Computer Vision EAI/Springer Innovations in Communication and Computing, 2023 DOI:10.1007/978-3-031-20541-5_16

[4]	B-PSA: A Binary Pendulum Search Algorithm for the Feature Selection Problem Computers, 2023 DOI:10.3390/computers12120249

[5]	Gene subset selection using fuzzy statistical dependence technique and binary bat algorithm Journal of Dynamics and Games, 2022 DOI:10.3934/jdg.2022011

[6]	An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection International Journal of System Assurance Engineering and Management, 2022 DOI:10.1007/s13198-021-01598-7

[7]	Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) IEEE Access, 2021 DOI:10.1109/ACCESS.2021.3056407

[8]	Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification Computational and Mathematical Methods in Medicine, 2021 DOI:10.1155/2021/5588385

[9]	An Enhanced Binary Cuckoo Search Algorithm Using Crossover Operators for Features Selection 2021 International Conference on Advanced Computer Applications (ACA), 2021 DOI:10.1109/ACA52198.2021.9626811

[10]	An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine Applied Soft Computing, 2020 DOI:10.1016/j.asoc.2020.106266

[11]	Breast Cancer Detection via Mammographic Images : A Survey International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020 DOI:10.32628/CSEIT20633

[12]	Gene Selection Using Hybrid Multi-Objective Cuckoo Search Algorithm With Evolutionary Operators for Cancer Microarray Data IEEE Access, 2020 DOI:10.1109/ACCESS.2020.3029890

[13]	Optimized Model for Cervical Cancer Detection Using Binary Cuckoo Search Recent Patents on Computer Science, 2019 DOI:10.2174/2213275911666181120092223

[14]	Extracting tumor in MR brain and breast image with Kapur’s entropy based Cuckoo Search Optimization and morphological reconstruction filters Biocybernetics and Biomedical Engineering, 2018 DOI:10.1016/j.bbe.2018.07.005

[15]	Modified Binary Cuckoo Search for Feature Selection: A Hybrid Filter-Wrapper Approach 2017 13th International Conference on Computational Intelligence and Security (CIS), 2017 DOI:10.1109/CIS.2017.00113

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