Deriving tumor detection models using convolutional neural networks from MRI of human brain scans

被引：15

作者：

Kalaiselvi T. ^{[1
]}

Padmapriya S.T. ^{[1
]}

Sriramakrishnan P. ^{[2
]}

Somasundaram K. ^{[1
]}

机构：

[1] Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, 624302, Tamil Nadu

[2] Department of Computer Applications, Kalasalingam Academy of Research and Education (Deemed to be University), Krishnankoil, 626126, Tamil Nadu

来源：

International Journal of Information Technology | 2020年 / 12卷 / 2期

关键词：

Brain tumor; BraTS2013; CNN models; Neural networks; Tumor detection;

D O I：

10.1007/s41870-020-00438-4

中图分类号：

学科分类号：

摘要：

Convolutional Neural Networks (CNNs) is a deep learning model used for image classification. The objective of our work is to analyze the different CNN models in the classification of benign tumor slices from MRI brain volumes and select a suitable CNN model for brain tumor detection. In this paper, we have developed six CNN models and trained using BraTS2013 dataset and tested with the WBA dataset. We have inferred the results for all the six models. The best model for classification of tumor slices is found among the six models. The accuracy of each and every model is recorded. Our models have attained about 96–99% of accuracy. © 2020, Bharati Vidyapeeth's Institute of Computer Applications and Management.

引用

页码：403 / 408

页数：5

共 14 条

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