Research Article
Image Classification of Brain Tumors Using Improved CNN Framework with Data Augmentation
@INPROCEEDINGS{10.1007/978-3-030-64214-3_6, author={Xin Ning and Zhanbo Li and Haibo Pang}, title={Image Classification of Brain Tumors Using Improved CNN Framework with Data Augmentation}, proceedings={Mobile Computing, Applications, and Services. 11th EAI International Conference, MobiCASE 2020, Shanghai, China, September 12, 2020, Proceedings}, proceedings_a={MOBICASE}, year={2020}, month={12}, keywords={Mobile medical equipment Multi-grade brain tumor classification Data augmentation CNN MRI Adam}, doi={10.1007/978-3-030-64214-3_6} }- Xin Ning
Zhanbo Li
Haibo Pang
Year: 2020
Image Classification of Brain Tumors Using Improved CNN Framework with Data Augmentation
MOBICASE
Springer
DOI: 10.1007/978-3-030-64214-3_6
Abstract
At present, the problem of shortage of medical human resources can be solved through mobile medical equipment, the main method to improve the diagnostic performance of mobile medical equipment is to improve the accuracy of the algorithm. Brain tumor classification is to determine the tumor type of patients. The accurate brain tumor classification algorithm can improve the diagnostic performance of mobile medical equipment while assisting doctors in diagnosis. This paper proposes a multi-grade brain classification system using improved CNN framework with extensive data augmentation for differentiating among glioma, meningioma and pituitary tumors, which from three prominent types of brain tumor. First, we locate the tumor and extract the region of interest (ROI). Secondly, to solve the problem of insufficient data samples in the brain tumor classification, we use data augmentation techniques to augment the data samples. Thirdly, VGG-19 and Inception V3 model are improved, and the CNN model is optimized by Adam algorithm. Finally, the improved CNN framework is trained and classified with augmented dataset. Experimental results show that the system proposed in this paper based on data augmentation and improved CNN framework has better classification performance than traditional classifier, and this system can effectively solves the problem of low accuracy caused by insufficient data samples.
