Machine Learning model for Breast Cancer Prediction

Ankur Gupta, Dushyant Kaushik, Muskan Garg, Apurv Verma
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引用次数: 1

Abstract

The work which has been presented here mainly concentrates on the prediction of breast cancer. For this purpose, convolution neural network is used. In this work, previous records of breast cancer were taken in to account. Convolutional neural network has been used in the identification of breast cancer. In this method first of all pictures are organized. After that this organized picture is separated on the basis of its qualities. In the next step these pictures are developed in a new form and in the end prediction work is done. For the reduction of comparison time, space edge based pictures are taken. Because of that performance improves. In the introduction part of this work, fundamental ideology related to breast cancer prediction system is explained. In the next part of this work those researches are highlighted in which a lot of work is already done in the determination of breast cancer. Inspiration in addition to problem related research has been highlighted afterward. In the end, results of computerized calculation related to this research are shown. It has been clearly comes out of results that when edge based pictures are treated in convolution neural network, time and space reduced Which makes the performance of work better.
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乳腺癌预测的机器学习模型
这里介绍的工作主要集中在乳腺癌的预测上。为此,使用卷积神经网络。在这项工作中,之前的乳腺癌记录被考虑在内。卷积神经网络已被用于乳腺癌的识别。在这种方法中,首先对所有图片进行组织。之后,这幅有组织的图画根据其性质被分开。下一步,将这些图像以一种新的形式展开,最后进行预测工作。为了减少比较时间,采用了基于空间边缘的图像。因此,性能得到了提高。在本文的引言部分,阐述了乳腺癌预测系统的基本思想。在这项工作的下一部分,这些研究将被重点强调,其中很多工作已经在确定乳腺癌方面完成了。除了问题相关的研究之外,启发也在之后得到了突出。最后给出了与本研究相关的计算机计算结果。结果表明,在卷积神经网络中处理基于边缘的图像,减少了时间和空间,提高了工作性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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