Design and Implementing Brain Tumor Detection Using Machine Learning Approach

G. Hemanth, M. Janardhan, L. Sujihelen
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引用次数: 60

Abstract

Nowadays, brain tumor detection has turned upas a general causality in the realm of health care. Brain tumor can be denoted as a malformed mass of tissue wherein the cells multiply abruptly and ceaselessly, that is there is no control over the growth of the cells. The process of Image segmentation is adopted for extracting abnormal tumor region within the brain. In the MRI (magnetic resonance image), segmentation of brain tissue holds very significant in order to identify the presence of outlines concerning the brain tumor. There is abundance of hidden information in stored in the Health care sector. With appropriate use of accurate data mining classification techniques, early prediction of any disease can be effectively performed. In the medical field, the techniques of ML (machine learning) and Data mining holds a significant stand. Majority of which is adopted effectively. The research examines list of risk factors that are being traced out in brain tumor surveillance systems. Also the method proposed assures to be highly efficient and precise for brain tumor detection, classification and segmentation. To achieve this precise automatic or semi-automatic methods are needed. The research proposes an automatic segmentation method that relies upon CNN (Convolution Neural Networks), determining small 3 × 3 kernels. By incorporating this single technique, segmentation and classification is accomplished. CNN (a ML technique) from NN (Neural Networks)wherein it has layer based for results classification. Various levels involved in the proposed mechanisms are: 1. Data collection, 2. Pre-processing, 3. Average filtering, 4. segmentation, 5. feature extraction, 6. CNN via classification and identification. By utilizing the DM (data mining) techniques, significant relations and patterns from the data can be extracted. The techniques of ML (machine learning) and Data mining are being effectively employed for brain tumor detection and prevention at an early stage.
利用机器学习方法设计和实现脑肿瘤检测
如今,脑肿瘤的检测已成为医疗保健领域的普遍因果关系。脑肿瘤可以被认为是一种畸形的组织团块,其中细胞突然地、不断地繁殖,也就是说,细胞的生长是无法控制的。采用图像分割的方法提取脑内的异常肿瘤区域。在核磁共振成像(MRI)中,为了识别脑肿瘤的轮廓,脑组织的分割非常重要。有大量的隐藏信息存储在卫生保健部门。通过适当使用准确的数据挖掘分类技术,可以有效地进行任何疾病的早期预测。在医学领域,ML(机器学习)和数据挖掘技术占有重要地位。其中大部分被有效采纳。这项研究检查了脑肿瘤监测系统中正在追踪的一系列风险因素。该方法保证了脑肿瘤检测、分类和分割的高效性和准确性。要做到这一点,就需要采用自动或半自动的方法。本研究提出了一种基于CNN(卷积神经网络)的自动分割方法,确定小的3 × 3核。通过结合这一单一技术,完成了分割和分类。CNN(一种ML技术)来自NN(神经网络),其中它具有基于结果分类的层。拟议机制所涉及的各个层面是:2.数据收集;预处理、3。平均过滤,4。分割,5。6.特征提取;CNN通过分类和识别。利用DM(数据挖掘)技术,可以从数据中提取重要的关系和模式。ML(机器学习)和数据挖掘技术正被有效地用于脑肿瘤的早期检测和预防。
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