Analysis of Brain Tumor Disease Detection Using Convolutional Neural Network

Yogesh S. Deshmukh, Samiksha Dahe, Tanmayeeta Belote, Aishwarya Gawali, Sunnykumar Choudhary
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Abstract

Brain Tumor detection using Convolutional Neural Network (CNN) is used to discover and classify the types of Tumor. Over a amount of years, many researchers are researched and planned ways throughout this area. We’ve proposed a technique that’s capable of detecting and classifying different types of tumor. For detecting and classifying tumor we have used MRI because MRI images gives the complete structure of the human brain, without any operation it scans the human brain and this helps in processing of image for the detection of the Tumor. The prediction of tumor by human from the MRI images leads to misclassification. This motivates us to construct the algorithm for detection of the brain tumor. Machine learning helps and plays a vital role in detecting tumor. In this paper, we tend to use one among the machine learning algorithm i.e. Convolutional neural network (CNN), as CNNs are powerful in image processing and with the help of CNN and MRI images we designed a framework for detection of the brain tumor and classifying its Different types.
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卷积神经网络在脑肿瘤疾病检测中的应用分析
脑肿瘤检测采用卷积神经网络(CNN)来发现和分类肿瘤的类型。多年来,许多研究人员对这一领域进行了研究和规划。我们提出了一种能够检测和分类不同类型肿瘤的技术。为了检测和分类肿瘤,我们使用核磁共振成像,因为核磁共振成像图像给出了人类大脑的完整结构,不需要任何操作,它扫描人类大脑,这有助于处理图像,以检测肿瘤。人类根据MRI图像对肿瘤的预测会导致误分类。这促使我们构建检测脑肿瘤的算法。机器学习在肿瘤检测中起着至关重要的作用。在本文中,我们倾向于使用机器学习算法中的一种,即卷积神经网络(CNN),因为CNN在图像处理方面具有强大的功能,我们借助CNN和MRI图像设计了一个框架来检测脑肿瘤并对其进行不同类型的分类。
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