基于卷积神经网络的脑肿瘤MR图像检测与分割

2021 Second International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) Pub Date : 2021-12-16 DOI:10.1109/ICSTCEE54422.2021.9708592

Valaparla Rohini, Kuchipudi Prasanth Kumar

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引用次数: 1

摘要

脑肿瘤是影响人类的疾病之一。它是一种恶性肿瘤。脑瘤是大脑中生长失控的异常脑细胞。这种疾病会影响许多人，在大群体中可能很难生存。当允许人们对脑肿瘤进行早期发现时，将有助于人们的生存，降低人们的死亡率。脑异常细胞形成的检测是医学影像学中的一个难点。检测是通过使用磁共振成像(MRI)完成的。本文提出了一种基于迁移学习的卷积神经网络结构来检测肿瘤，其目的是利用感兴趣区域和非感兴趣区域来区分肿瘤区域。数据集取自开源Kaggle存储库。该模型在测试数据集上获得了98.1%的准确率。这个模型完成了最先进的工作。

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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ConvNet Based Detection and Segmentation of Brain Tumor from MR Images

One of the diseases that affects humans is brain tumor. It is a type of malignancy disease. A brain tumor is aberrant brain cells that has grown out of control in the brain. This sickness affects many people, and it might be difficult to survive in large groups. When allowing people for early detection of brain tumor, it will help to survive and reduce the death rate of people. Detection of aberrant cells formation in brain is very difficult in medical imaging. The Detection is done by using magnetic resonance imaging (MRI). In this paper, ConvNet architecture is proposed with transfer learning to detect tumor and it aims to differentiate the tumor area by using ROI and non-ROI. The data set is taken from open source Kaggle repository. This model obtained 98.1% accuracy on test data set. This model performed state of the art work.

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来源期刊

2021 Second International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)

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