Big data simulation software for breast cancer growth repository system

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 environment 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 comprehensive breast cancer screening; breast cancer diagnosis; and clinical assessments and treatment

A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases

Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.Peer ReviewedPostprint (published version

A Genetic Algorithm Based Feature Selection for Classification of Brain MRI Scan Images Using Random Forest Classifier

A brain tumour is a mass of tissue that is formed by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Magnetic Resonance Imaging is a useful imaging technique that is widely used by physicians to investigate different pathologies. After a long clinical research, it is proved to be harmless. Improvement in computing power has introduced Computer Aided Diagnosis (CAD) which can efficiently work in an automated environment. Diagnosis or classification accuracy of such a CAD system is associated with the selection of features. This paper proposes an enhanced brain MRI image classifier targeting two main objectives, the first is to achieve maximum classification accuracy and second is to minimize the number of features for classification. Feature selection is performed using Genetic Algorithm (GA) while classifiers used are Random forest Classifier

A review on a deep learning perspective in brain cancer classification

AWorld Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, andWilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm