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Breast Tumor Segmentation: Comparison of Fast & Furious Bayesian Network (FFBN)

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Matlab Code for Breast Tumor Segmentation: Comparison of Fast & Furious Bayesian Network (FFBN) and Support Vector Machine (SVM) Algorithms

Description

The main objective of segmentation is to locate tumor and other abnormalities. The framework is a combination of image processing techniques to segment breast and fibro glandular tissue, to automatically compute breast density in breast MRI.

Input

Breast MRI Image

Output

Tumor Identification of the Segmented Image

Tags

#Bayesian, #network, #Support, #Vector, #Machine, # imputation, # Output, #survival, #Breast, #Tumour, #Segmentation, #main, #objective, #segmentation, #locate, #tumor, #other, #abnormalities, #framework, #combination, #image, #processing, #techniques, #segment, #breast, #fibro, #glandular, #tissue, #automatically, #compute, #breast, #density, #breast, #MRI,

Reference

[1]K. Jayasurya et al., "Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy", Medical Physics, vol. 37, no. 4, pp. 1401-1407, 2010. Available: 10.1118/1.3352709. [2]C. Pucci, C. Martinelli and G. Ciofani, "Innovative approaches for cancer treatment: current perspectives and new challenges", ecancermedicalscience, vol. 13, 2019. Available: 10.3332/ecancer.2019.961. [3]H. Seada, M. Abouhawwash and K. Deb, "Multiphase Balance of Diversity and Convergence in Multiobjective Optimization", IEEE Transactions on Evolutionary Computation, vol. 23, no. 3, pp. 503-513, 2019. Available: 10.1109/tevc.2018.2871362.