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Fusion: Practice and Applications
Volume 9 , Issue 2, PP: 48-61 , 2022 | Cite this article as | XML | Html |PDF

Title

Breast cancer Classification with Multi-Fusion Technique and Correlation Analysis

Authors Names :   Vandana Roy   1 *  

1  Affiliation :  Gyan Ganga Institute of Technology and Sciences, Jabalpur, India

    Email :  vandanaroy@ggits.org



Doi   :   https://doi.org/10.54216/FPA.090204

Received: June 10, 2022 Accepted: September 17, 2022

Abstract :

Breast cancer is responsible for the deaths of the vast majority of women who succumb to the disease. By detecting and treating the disease at an earlier stage, it is feasible to reduce the mortality rate associated with breast cancer. Mammography is the technique of breast cancer screening that has received the most amount of approval from the medical community. Imaging of the ipsilateral projections of the bilateral (right and left) breasts, also known as mediolateral oblique (MLO) and craniocaudal (CC) views, is often necessary for this surgery. This imaging technique is also known as the craniocaudal projection. Sonography, which is also known as ultrasound imaging, is used in combination with mammography during the diagnostic phase (which occurs after the screening phase) to offer a more accurate examination of any abnormalities that may have been detected. Radiologists may be able to make a more precise diagnosis of breast cancer by carrying out an objective assessment with the assistance of CAD systems. CAD is an abbreviation that stands for computer-aided detection and diagnosis. Researchers have proposed computer-aided design (CAD) systems as a viable technique for increasing system performance. These CAD systems take information from a variety of sources and combine it into a single database. In the majority of occurrences, this necessitates the inclusion of qualities or evaluations that were collected from a wide range of information sources. Fusion of choices is effective when dealing with sources that are statistically independent, while fusion of characteristics is good when dealing with sources that have a significant degree of correlation with one another. However, sources often contain a mix of information that is associated with one another as well as information that is independent of one another; as a consequence, none of these approaches is the greatest choice available to choose from. The development of optimal fusion strategies for Multiview and multimodal breast CAD systems is the major focus of this thesis. Canonical correlation analysis is the name of the statistical approach that serves as the foundation for these tactics (CCA). The CCA algorithm alters two multivariate datasets in such a manner as to maximize the correlation that already exists between them. This, in turn, optimizes the feature fusion that occurs after the CCA method has been applied. On the other hand, the performance of benchmark fusion schemes that combine all three sources of information is only at most equivalent to the performance of benchmark schemes that fuse two information sources. In addition, the performance of benchmark fusion schemes that combine all three sources of information is worse than the performance of CCA-based feature fusion schemes that combine two sources of information. This indicates that even if increasing the number of sources could bring new information, only a fusion approach that is optimized to exploit its maximum potential would be able to make the most of this extra data. In conclusion, the CCA-based fusion schemes exhibit robustness when tested against a wide array of performance indicators, datasets, information sources, and diagnostic tasks that are related to the diagnosis of breast cancer. The benchmark fusion techniques, on the other hand, do not demonstrate this resilience.

Keywords :

wavelet-based image fusion; sum absolute difference; hazy images; Kalman filter.

References :

[1] Amjath Ali, J & Janet, J 2013,‘Discrete Shearlet Transform Based Classification of Microcalcification in

Digital Mammograms’, Journal of Computer Applications, vol. 6, no. 1, pp. 19-21.

[2] Anju Dahiya & Dubey, RB 2014,‘Survey of some Multilevel thresholding techniques for Medical

Imaging’, International Journal of Scientific Engineering and Research, vol. 3, no. 7, pp. 103-104.

[3] Ayman A AbuBaker 2015,‘Automatic Microcalcification Detection Using WaveletTransform’,

International Journal of Computer Theory and Engineering, vol. 7, no. 1, pp. 40-45.

[4] Baljit Singh Khehra & Amar Partap Singh Pharwaha 2013.‘LeastSquares Support Vector Machine for

Characterization of Clusters of Microcalcifications’, World Academy of Science, Engineering &

Technology, International Journal of Computer, Electrical, Automation, Control and Information

Engineering, vol. 7, no. 12, pp. 1612-1621.

[5] Barakat, N& Bradley, AP 2010, ‘Rule extraction from support vector machines: A review’,

Neurocomputing, vol. 74, pp. 178-190.

[6] Baykasoglu, A & Ozsoydan, FB 2014, ‘An improved firefly algorithm for solving dynamic

multidimensional knapsack problems’, Expert Systems with Applications, vol. 41, pp. 3712-3725.

[7] Beena Ullala Mata, BN& Meenakshi, M 2011, ‘A Novel Approach for Automatic Detection of

Abnormalities in Mammograms’, IEEE International conference on Recent Advances in Intelligent

Computational Systems, pp. 831 - 836.

[8] Bekaddour Fatima &Chikh Mohammed Amine 2012, ‘A Neuro-fuzzy Inference model for Breast cancer

recognition’, International Journal of Computer Science & Information Technology, vol. 4, no. 5, pp. 163-

173. 165

[9] Bhattacharya, M & Das, A 2010,‘Genetic Algorithm Based Feature Selection In a Recognition Scheme

Using Adaptive Neuro Fuzzy Techniques’, Int J Comput Commun Control, vol. 5, pp. 458-468.

[10] Birbil, SI, Fang, SC & Sheu, RL 2014, ‘On the convergence of a population-based global optimization

algorithm’, Journal of Global Optimization, vol. 30, no. 2, pp. 301-318.

[11] Casti, P, Mencattini, A, Salmeri, M & Rangayyan, RM 2015,‘Analysis of Structural Similarity in

Mammograms for Detection of Bilateral Asymmetry’, IEEE Transactions on Medical Imaging, vol. 34, no.

2, pp. 662-671.

[12] Cevikalp, H 2010,‘New clustering algorithms for the support vector machine based hierarchical

classification’, Pattern Recognition Letters, vol. 31, pp. 1285-1291.

[13] Chaudhuri, A & De, K 2011, ‘Fuzzy Support Vector Machine for bankruptcy prediction’, Applied Soft

Computing, vol. 11, pp. 2472-2486.

[14] Chou, JS, Cheng, MY, Wu, YW & Pham, AD 2014,‘Optimizing parameters of support vector machine

using fast messy genetic algorithm for dispute classification’, Expert Systems with Applications, vol. 41,

pp. 3955-3964.

[15] Da Cunha, ALZhou, J & Do, MN 2006,‘The Nonsubsampled Contourlet Transform: Theory, Design and

Applications’, IEEE Trans. Image Process, vol. 15, pp. 3089-3101.

[16] Das, A & Bhattacharya, M 2010,‘Identification of microcalcifications and grading of masses using digital

mammogram’, Int J Med Eng Inform, vol. 2, pp. 122-138.

[17] Das, A & Bhattacharya, M 2011,‘Computerized decision support system for mass identification in breast

using digital mammogram: A study on GA based Neuro-fuzzy approaches’, Adv Exp Med Biol, vol. 696,

pp.523-533.

[18] Deepika & Mary Sindhuja, NM 2014,‘Performance Analysis of Image Fusion Algorithms using HAAR

Wavelet’, International Journal of Computer Science and Mobile Computing, vol. 3, no. 1, pp. 487-494.

166

[19] De Paredes & Ellen Shew, 2007,’Atlas of Mammography’, Third Edition, Lippincott Williams & Wilkins.

[20] Dheeba, J& Wiselin Jiji, G 2010,‘Detection of Microcalcification Clusters in Mammograms using Neural

Network’, International Journal of Advanced Science and Technology, vol. 19, pp 13-22.

[21] Diego Oliva, Erik Cuevas, Gonzalo Pajares, Daniel Zaldivar and Perez-Cisneros, M 2013,‘Multilevel

Thresholding Segmentation Based on Harmony Search Optimization’, Journal of Applied Mathematics, pp.

1 - 24.

[22] Diego Oliva, Erik Cuevas, Gonzalo Pajares, Daniel Zaldivar & Valentín Osuna 2014, ‘A Multilevel

thresholding algorithm using Electro-Magnetism Optimization’, Neurocomputing, vol. 139, pp. 357-381.

 


Cite this Article as :
Vandana Roy, Breast cancer Classification with Multi-Fusion Technique and Correlation Analysis, Fusion: Practice and Applications, Vol. 9 , No. 2 , (2022) : 48-61 (Doi   :  https://doi.org/10.54216/FPA.090204)