Particle swarm optimization based fusion of ultrasound echographic and elastographic texture features for improved breast cancer detection

2019 ◽  
Vol 42 (3) ◽  
pp. 677-688 ◽  
Author(s):  
S. Sasikala ◽  
M. Bharathi ◽  
M. Ezhilarasi ◽  
Sathiya Senthil ◽  
M. Ramasubba Reddy
Keyword(s):  
Breast Cancer ◽  
Particle Swarm Optimization ◽  
Cancer Detection ◽  
Particle Swarm ◽  
Texture Features ◽  
Breast Cancer Detection ◽  
Swarm Optimization

An Early Breast Cancer Detection System Using Stacked Auto Encoder Deep Neural Network with Particle Swarm Optimization Based Classification Method

2021 ◽  
Vol 11 (12) ◽  
pp. 2897-2906
Author(s):  
K. Sangeetha ◽  
S. Prakash
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Particle Swarm Optimization ◽  
Early Detection ◽  
Contrast Enhancement ◽  
Cancer Detection ◽  
Detection System ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization

The demand in breast cancer’s early detection and diagnosis over the last few decade has given a new research avenues. For an individual who is suffered from breast cancer, a successful treatment plan can be specified if early stage diagnosis of non-communicable disease is done as stated by world health organization (WHO). Around the world, mortality can be reduced by cure disease’s early diagnosis. For breast cancer’s early detection and to detect other abnormalities of human breast tissue, digital mammogram is used as a most popular screening method. Early detection is assisted by periodic clinical check-ups and self-tests and survival chance is significantly enhanced by it. For mammograms (MGs), deep learning (DL) methods are investigated by researchers due to traditional computer-aided detection (CAD) systems limitations and breast cancer’s early detection’s extreme importance and patients false diagnosis high impact. So, there is need to have a noninvasive cancer detection system which is efficient, accurate, fast and robust. There are two process in proposed work, Histogram Rehabilitated Local Contrast Enhancement (HRLCE) technique is used in initial process for contrast enhancement with two processing stages. Contrast enhancements potentiality is enhanced while preserving image’s local details by this technique. So, for cancer classification, Particle Swarm Optimization (PSO) and stacked auto encoders (SAE) combined with framework based on DNN called SAE-PSO-DNN Model is used. The SAE-DNN parameters with two hidden layers are tuned using PSO and Limited-memory BFGS (LBFGS) is used as a technique for reducing features. Specificity, sensitivity, normalized root mean square erro (NRMSE), accuracy parameters are used for evaluating SAE-PSO-DNN models results. Around 92% of accurate results are produced by SAE-PSO-DNN model as shown in experimentation results, which is far better than Convolutional Neural Network (CNN) as well as Support Vector Machine (SVM) techniques.

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

2010 ◽  
Vol 36 (3) ◽  
pp. 1503-1510 ◽  
Author(s):  
U. Rajendra Acharya ◽  
E. Y. K. Ng ◽  
Jen-Hong Tan ◽  
S. Vinitha Sree
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Texture Features ◽  
Breast Cancer Detection ◽  
Support Vector

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.

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