Big data simulation software for breast cancer growth repository system

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The development of the simulation software aims at anticipating the growth of breast cancer. Based on certain numerical iterative methods, this simulation works with discretization and Partial Differential Equation (PDE). As a class of Helmholtz equations, PDE approach are known to govern the growth of this type of cancer. Considering both time and place, the Helmholtz equation’s accuracy visualizes breast cancer and its growth. This growth is of breast cancer is captured and the convergence results in sequential and parallel computing environ-ment is expressed through the numerical libraries available in the repository system. Currently, both the parallel performance measurement and Numerical analysis that involve execution time, speedup, efficiency, effectiveness and temporal performance are being investigated. The process of breast cancer visualization requires a huge memory and expensive calculations. It is observed that both the distributed memory and distributed processors of the parallel computer systems development were required in most of the studies conducted on the growth of this cancer. It is considered as an important computation platform needed to the development of parallel repository system leading to an increase in the speed and a decrease in the cost. The simulation software has several beneficial characteristics such as high performance estimation, multidimensional visualization of breast cancer and being friendly. It also provides a real time solution and strength. This soft-ware is expected to increase the level of confidence in terms of computer-aided decision making which can be reflected positively on com-prehensive breast cancer screening; breast cancer diagnosis; and clinical assessments and treatment.

     

     


  • Keywords


    Breast Cancer Growth; Simulation Software Development; Repository Library; Numerical Methods; Helmholtz Equation

  • References


      [1] Ferlay J, Bray F, Pisani P, Parkin DM. Globocan 2002: Cancer incidence, mortality and prevalence worldwide. IARC Cancer Base 2004 (5).

      [2] Alias N, Islam MR, Rosly S. A dynamic PDE solver for breasts’ cancerous visualization on distributed parallel computing systems. Proceedings of the IASTED International Conference on Advances in Computer Science and Engineering, ACSE 2009: 138-143.

      [3] Alias N, Islam MR, Shahir R, Hamzah H, Satam N, Safiza Z, Darwis R, Ludin E, Azami M. Parallel system for abnormal cell growth prediction based on fast numerical simulation. 6083 LNCS 2010: 222-231.

      [4] Wang, Q., et al., Classification of brain tumors in MR images. Pennsylvania State University, 2009.

      [5] Khotanlou, H., et al., 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets and Systems, 2009. 160 (10): p. 1457-1473. https://doi.org/10.1016/j.fss.2008.11.016.

      [6] Yang, W. and M. Siliang. Automatic detection and segmentation of brain tumor using fuzzy classification and deformable models. In Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on. 2011. IEEE.

      [7] Choubey, A., G. Sinha, and S. Choubey. A hybrid filtering technique in medical image denoising: Blending of neural network and fuzzy inference. inElectronics Computer Technology (ICECT), 2011 3rd International Conference on. 2011. IEEE.

      [8] Alamelumangai, N. and J. DeviShree. An ultrasound image preprocessing system using memetic ANFIS method. In Proceedings of the 2010 International Conference on Biology, Environment and Chemistry, (ICBEC’11), IACSIT Press, Singapore. 2011.

      [9] Khalid, N.E.A., S. Ibrahim, and M. Manaf. Brain abnormalities segmentation performances contrasting adaptive network-based fuzzy inference system (ANFIS) vs K-nearest neighbors (k-NN) vs fuzzy c-means (FCM). In 15th WSEAS International Conference on Computers. 2011.

      [10] Noor, N.M., et al. Adaptive neuro-fuzzy inference system for brain abnormality segmentation. In Control and System Graduate Research Colloquium (ICSGRC). 2010 IEEE. 2010. IEEE.

      [11] Balafar, M.A., et al., Review of brain MRI image segmentation methods. Artificial Intelligence Review, 2010. 33(3): p. 261-274. https://doi.org/10.1007/s10462-010-9155-0.

      [12] Gallea, R., et al. Noise filtering using edge-driven adaptive anisotropic diffusion. In Computer-Based Medical Systems, 2008. CBMS'08. 21st IEEE International Symposium on. 2008. IEEE.

      [13] Smolka, B. Modified biased anisotropic diffusion processing of noisy color images. In Signal Processing, 2008. ICSP 2008. Ninth International Conference on. 2008. IEEE.

      [14] Bauer, S., L.-P. Nolte, and M. Reyes, Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization, in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011.2011, Springer. p. 354-361.

      [15] Devasena, C.L. and M. Hemalatha, Hybrid Image Classification Technique to Detect Abnormal Parts in MRI Images, in Computational Intelligence and Information Technology.2011, Springer. p. 200-208.

      [16] Ain, Q., M.A. Jaffar, and T.-S. Choi, Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor. Applied Soft Computing, 2014. 21 p. 330-340. https://doi.org/10.1016/j.asoc.2014.03.019.

      [17] Wijaya, I., K. Uchimura, and Z. Hu. Face recognition based on dominant frequency features and multiresolution metric. In Innovative Computing, Information and Control, 2007. ICICIC'07. Second International Conference on. 2007. IEEE.

      [18] Ohno, A. and H. Murao. A similarity measuring method for images based on the feature extraction algorithm using reference vectors. In Innovative Computing, Information and Control, 2007. ICICIC'07. Second International Conference on. 2007. IEEE.

      [19] Xu, Y. and F. Song, Feature extraction based on a linear separability criterion. International Journal of Innovative Computing, Information and Control, 2008. 4(4): p. 857-865.

      [20] Scholkopft, B. and K.-R. Mullert, Fisher discriminant analysis with kernels. Neural networks for signal processing IX, 1999. 1: p. 1.

      [21] Mun, G.-J., B.-N. Noh and Y.-M. Kim, Enhanced stochastic learning for feature selection in intrusion classification. International Journal of Innovative Computing, Information and Control, 2009. Five (11).

      [22] Gladis Pushpa Rathi, V. and S. Palani, Brain tumor MRI image classification with feature selection and extraction using Linear Discriminant analysis. arXiv preprint arXiv:1208.2128, 2012.

      [23] Rathi, V. and S. Palani, Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis. arXiv preprint arXiv:1208.2128, 2012.

      [24] Tiwari, M.V. and D. Chaudhari, an Overview of Automatic Brain Tumor Detection from Magnetic Resonance Images. International Journal of Advanced Research in Engineering & Technology (IJARET), 2013. 4(2): p. 61-68.

      [25] Gunnarsson T. Microwave imaging of biological tissues: applied towards breast cancer tumor growth detection. Thesis 2007. Department of Compuhttps://doi.org/10.1016/j.fss.2008.11.016 ter Science and Electronics, Malaridalen University Vasteras.

      [26] Alias N, Ghani ACA., Saipansaipol HF., Ramli N, Palil SQM. Wave equation for early detection of breast cancer growth using MATLAB Distributed Computing. 2012 International Conference on Enabling Science and Nanotechnology, ESciNano 2012 - Proceedings 2012.

      [27] Alias, N., Alwesabi, Y., and Al-Rahmi, W. M. (2017). Chronology Of Brain Tumor Classification Of Intelligent Systems Based On Mathematical Modeling, Simulation And Image Processing Techniques. Journal of Theoretical & Applied Information Technology, 95(19).

      [28] Alias N, Saipansaipol HF, Ghani ACA. Chronology of DIC technique based on the fundamental mathematical modeling and dehydration impact. Journal of Food Science and Technology 2012:1-11.


 

View

Download

Article ID: 15155
 
DOI: 10.14419/ijet.v7i4.15155




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.