scholarly journals Features Selection Study for Breast Cancer Diagnosis Using Thermographic Images, Genetic Algorithms, and Particle Swarm Optimization

2021 ◽  
Vol 11 (2) ◽  
pp. 1-18
Author(s):  
Amanda Lays Rodrigues da Silva ◽  
Maíra Araújo de Santana ◽  
Clarisse Lins de Lima ◽  
José Filipe Silva de Andrade ◽  
Thifany Ketuli Silva de Souza ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Particle Swarm Optimization ◽  
Digital Image Analysis ◽  
Particle Swarm ◽  
Breast Cancer Diagnosis ◽  
Classification Systems ◽  
Features Selection ◽  
Treatment Efficiency ◽  
Swarm Optimization ◽  
Main Method

Early detection of breast cancer is critical to improve treatment efficiency and chance of cure. Mammography is the main method for breast cancer screening; however, it has some limitations. Infrared thermography is a technique that is being studied for its benefits. The existing tumor classification systems are detailed, complex, and have low usability. Therefore, combining specialized professionals with methods of digital image analysis using thermography can help improve the diagnosis. Considering this, some computational areas are working on studies and creating methods to assess these data. The features selection plays a key role in this process, as it is a way to help solving data multidimensionality problems. This study aims to reduce the amount of features from thermographic images with mammary lesions. The authors used genetic algorithm and particle swarm optimization for features selection and compared the performance of each method to the performance using the entire set of features.

scholarly journals Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting

2020 ◽  
Vol 16 (11) ◽  
pp. 155014772097150
Author(s):  
Vijayalakshmi S ◽  
John A ◽  
Sunder R ◽  
Senthilkumar Mohan ◽  
Sweta Bhattacharya ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Particle Swarm Optimization ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Particle Swarm ◽  
Kernel Density ◽  
Swarm Optimization ◽  
Cancer Prediction ◽  
Proposed Model ◽  
Sensitivity Specificity

Cancer is enlisted as the second leading reason for death across the world wherein almost one person out of six dies of cancer. Breast cancer is one of the most common forms of cancer predominant in women having the second highest mortality rate in the world. Various scientific studies have been conducted to combat this disease, and machine learning approaches have been an extremely popular choice. Particle swarm optimization has been identified as one of the most powerful and efficient technique for the diagnosis of breast cancer guiding physicians towards timely and accurate treatment. It is also pertinent to mention that multi-modal prediction methods are used to make decisions depending upon different scenarios and aspects whereas the non-dominating sorting feature is useful to sort different objects based on differing requirements. The main novelty of this work is multi-modal prediction algorithm for breast cancer prediction is proposed. The work encompasses the use of particle swarm optimization, non-dominating sorting and multi-classifier techniques, namely, k-nearest neighbour method, fast decision tree and kernel density estimation. Finally, Bayes’ theorem is implemented for revising the results to achieve optimum accuracy in the breast cancer prediction. The proposed particle swarm optimization and non-domination sorting with classifier technique model helps to select the most significant features relevant to breast cancer predictions. The selected features design the objective of the problem model. The proposed model is implemented on the WBCD and WDBC breast cancer data sets publicly available from the UCI machine learning data repository. The metrics considered are sensitivity, specificity, accuracy and time complexity. The experimental results of the study using measures such as sensitivity, specificity, accuracy and time complexity. The experimental results of the study are evaluated against the state-of-the-art algorithms, namely, genetic algorithm kernel density estimation and particle swarm optimization kernel density estimation wherein the results justify the superiority of the proposed model.

scholarly journals Double-Bottom Chaotic Map Particle Swarm Optimization Based on Chi-Square Test to Determine Gene-Gene Interactions

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Cheng-Hong Yang ◽  
Yu-Da Lin ◽  
Li-Yeh Chuang ◽  
Hsueh-Wei Chang
Keyword(s):  
Breast Cancer ◽  
Particle Swarm Optimization ◽  
Statistical Methods ◽  
Disease Susceptibility ◽  
Particle Swarm ◽  
Gene Interaction ◽  
High Order ◽  
Chi Square ◽  
Data Set ◽  
Swarm Optimization

Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0;Pvalue<0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer.

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