Hybrid Wolf Pack Algorithm and Particle Swarm Optimization Algorithm for Breast Cancer Diagnosis
Hybrid WPA and PSO Algorithm
Keywords:
Breast Cancer, Feature Extraction, Mamaogram Image, Neural Network, SegmentationAbstract
In women, breast cancer is deadly disease which is increased the death rate of women. By exploiting the mammogram images, a precise and early recognition of breast cancer is a complex task. Therefore, a new breast cancer recognition technique was proposed that considered five important stages: segmentation, preprocessing, feature extraction, feature selection as well as classification. Initially, by exploiting the median filtering as well as Contrast Limited Adaptive Histogram Equalization (CLAHE), input mammogram images are preprocessed. Subsequently, through the region growing method, the preprocessed images are fed to segmentation. Then, from the segmented image, texture, geometric and gradient features are extracted. The feature vector length is higher, it is important to choose optimal features. Moreover, the optimal features chosen are performed using the proposed optimization method. After completing the selection of the optimal features, they are fed to the classification procedure including the Neural Network (NN) classifier. As an innovation, to improve the precision of diagnosis (benign as well as malignant), the NN weight is chosen optimally. The NN weight optimization and the optimal feature selection are attained using the Hybrid Wolf Pack Algorithm (WPA) and Particle Swarm Optimization (PSO) Algorithm called the Hybrid WPA-PSO algorithm. At last, the performance analysis is performed between the proposed and conventional techniques.
References
Mohamed Hisham ArefIbrahim H. AboughalebYasser H. El-Sharkawy,"Custom optical imaging system for exvivo breast cancer detection based on spectral signature",Surgical Oncology 4 November 2020.
D. GhiehC. SaadeG. Berjawi,"Staying abreast of imaging - Current status of breast cancer detection in high density breast", Radiography 28 June 2020.
Meha DesaiManan Shah,"An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN)", Clinical eHealth 24 November 2020.
K. Park, W. Chen, M. A. Chekmareva, D. J. Foran and J. P. Desai, "Electromechanical Coupling Factor of Breast Tissue as a Biomarker for Breast Cancer," in IEEE Transactions on Biomedical Engineering, volume. 65, number. 1, page no. 96-103, Jan. 2018,
Ioannis SechopoulosJonas TeuwenRitse Mann,"Artificial intelligence for breast cancer detection in
mammography and digital breast tomosynthesis: State of the art", Seminars in Cancer BiologyAvailable online 9 June 2020.
IztokFister, IztokFisterJr, Xin-SheYang and JanezBrest, "A comprehensive review of firefly algorithms", Swarm and Evolutionary Computation, volume. 13, page no. 34-46, 2013.
Y. Chang, C. Jung, P. Ke, H. Song and J. Hwang, "Automatic Contrast-Limited Adaptive Histogram
Equalization With Dual Gamma Correction," IEEE Access, volume. 6, page no. 11782-11792, 2018.
Youlian Zhu, Cheng Huang,"An Improved Median Filtering Algorithm for Image Noise Reduction",2012 International Conference on Solid State Devices and Materials Science,Physics Procedia, page no.609-616, 2012.
J. Sanchez Hernandez, E. Martinez Izquierdo and A. Arquero Hidalgo, "Improving Parameters Selection of a Seeded Region Growing Method for Multiband Image Segmentation," IEEE Latin America Transactions, volume. 13, no. 3, page no. 843-849, March 2015.
X. Gao and P. Wan, "Target recognition of BP network based on Optimized wolf pack algorithm," 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, page no. 2123-2127, 2018.
N. Rokbani, A. Abraham and A. M. Alimi, "Fuzzy Ant Supervised by PSO and simplified ant supervised PSO applied to TSP," 13th International Conference on Hybrid Intelligent Systems (HIS 2013), Gammarth, Tunisia, page no. 251-255, 2013.
K.Srinivas,"Prediction of E-Learning Efficiency by Deep Learning in EKhool Online Portal Networks",,
Multimedia Research, Volume 3, Issue 4, October 2020.
Arvind Madhukar Jagtap,"Developing Deep Neural Network for Learner Performance Prediction in EKhool Online Learning Platform", Multimedia Research, Volume 3, Issue 4, October 2020.
