Wave equation for early detection of breast cancer growth using MATLAB Distributed Computing

Alias, Norma; Ghani, Asnida Che Abd.; Ramli, Nurelya; Palil, Siti Qatrunada Muhd

doi:10.1109/escinano.2012.6149682

Cited by 4 publications

(4 citation statements)

References 1 publication

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“…This sequential algorithm is illustrated in Figure 4 where the calculations flow to simulate the growth of breast cancer [26][27]. This takes place in three main steps.…”

Section: Sequential Algorithmmentioning

confidence: 99%

Big data simulation software for breast cancer growth repository system

Alias

Ghani²,

Saipo³

et al. 2018

IJET

Self Cite

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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.

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“…This sequential algorithm is illustrated in Figure 4 where the calculations flow to simulate the growth of breast cancer [26][27]. This takes place in three main steps.…”

Section: Sequential Algorithmmentioning

confidence: 99%

Big data simulation software for breast cancer growth repository system

Alias

Ghani²,

Saipo³

et al. 2018

IJET

Self Cite

View full text Add to dashboard Cite

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“…To anticipate cases of cancer, some of the approaches that have received application include Helmholtz equation type and mathematical modeling [3,4]. For the PDE under consideration, which has aided in disease anticipation in breast cancer, some of the models that have been used to solve it and give comprehensive insights include Alternating Group Explicit (AGE), Red Black Gauss Seidel (RBGS), and Jacobi (JB) methods [5,6]. It is also notable that most of the parallel and sequential algorithms associated with numerical techniques have relied on MATLAB for their implementation, especially by using the distributed computing software [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…It is also notable that most of the parallel and sequential algorithms associated with numerical techniques have relied on MATLAB for their implementation, especially by using the distributed computing software [7,8]. Also, the distributed parallel computer platform has been used to assist this software [6]. Indeed, the choice of the software has been determined by factors such as high speed processor performance and the huge memory allocation [9,10].…”

Section: Introductionmentioning

confidence: 99%

Repository System Development using Partial Differential Equations

Angriawan¹

2022

MSEA

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To visualize breast cancer, the procedure calls for expensive calculations, as well as a huge memory. In the majority of the previous investigations that have focused on breast cancer growth, they have reported the need for parallel computer systems development’s distributed processors, as well as the distributed memory. Therefore, when parallel repository systems are developed based on important computation platforms, there tends to be benefits such as decreased costs and increased speed. In this study, it was established that the proposed simulation software exhibits several strengths. They included being friendly, multidimensional breast cancer visualization, and high performance estimation. Also, the software, in the wake of big data dominance in the healthcare sector, offers strength and real-time solution. The implication is that the proposed model would offer promising results regarding computer-aided decision-making relative to the screening for breast cancer, as well as disease treatment, clinical assessments, and diagnosis. Hence, parallel algorithm could be used as an ideal model of solving sequential algorithm’s drawbacks, especially regarding time consumption.

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“…Other wellknown methods for early detection breast cancer are CAD (computer aided detection), clinical breast analysis, and blood tests. In order to cure breast cancer completely, it becomes very important to detect it early [3] [4].…”

Section: Introductionmentioning

confidence: 99%

Improved Framework for Breast Cancer Detection using Hybrid Feature Extraction Technique and FFNN

Alamin¹,

Jeberson²,

Bajaj³

2016

ijarai

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Abstract-Breast Cancer early detection using terminologies of image processing is suffered from the less accuracy performance in different automated medical tools. To improve the accuracy, still there are many research studies going on different phases such as segmentation, feature extraction, detection, and classification. The proposed framework is consisting of four main steps such as image preprocessing, image segmentation, feature extraction and finally classification. This paper presenting the hybrid and automated image processing based framework for breast cancer detection. For image preprocessing, both Laplacian and average filtering approach is used for smoothing and noise reduction if any. These operations are performed on 256 x 256 sized gray scale image. Output of preprocessing phase is used at efficient segmentation phase. Algorithm is separately designed for preprocessing step with goal of improving the accuracy. Segmentation method contributed for segmentation is nothing but the improved version of region growing technique. Thus breast image segmentation is done by using proposed modified region growing technique. The modified region growing technique overcoming the limitations of orientation as well as intensity. The next step we proposed is feature extraction, for this framework we have proposed to use combination of different types of features such as texture features, gradient features, 2D-DWT features with higher order statistics (HOS). Such hybrid feature set helps to improve the detection accuracy. For last phase, we proposed to use efficient feed forward neural network (FFNN). The comparative study between existing 2D-DWT feature extraction and proposed HOS-2D-DWT based feature extraction methods is proposed.

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Wave equation for early detection of breast cancer growth using MATLAB Distributed Computing

Cited by 4 publications

References 1 publication

Big data simulation software for breast cancer growth repository system

Big data simulation software for breast cancer growth repository system

Repository System Development using Partial Differential Equations

Improved Framework for Breast Cancer Detection using Hybrid Feature Extraction Technique and FFNN

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