Open Access

ARTICLE

A Multi-Category Brain Tumor Classification Method Bases on Improved ResNet50

Linguo Li^1,2, Shujing Li^1,*, Jian Su³

1 Fuyang Normal University, Fuyang, 236041, China
2 Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
3 University of Michigan, Ann Arbor, MI, 48109, USA

* Corresponding Author: Shujing Li. Email:

Computers, Materials & Continua 2021, 69(2), 2355-2366. https://doi.org/10.32604/cmc.2021.019409

Received 11 April 2021; Accepted 12 May 2021; Issue published 21 July 2021

Abstract

Brain tumor is one of the most common tumors with high mortality. Early detection is of great significance for the treatment and rehabilitation of patients. The single channel convolution layer and pool layer of traditional convolutional neural network (CNN) structure can only accept limited local context information. And most of the current methods only focus on the classification of benign and malignant brain tumors, multi classification of brain tumors is not common. In response to these shortcomings, considering that convolution kernels of different sizes can extract more comprehensive features, we put forward the multi-size convolutional kernel module. And considering that the combination of average-pooling with max-pooling can realize the complementary of the high-dimensional information extracted by the two structures, we proposed the dual-channel pooling layer. Combining the two structures with ResNet50, we proposed an improved ResNet50 CNN for the applications in multi-category brain tumor classification. We used data enhancement before training to avoid model over fitting and used five-fold cross-validation in experiments. Finally, the experimental results show that the network proposed in this paper can effectively classify healthy brain, meningioma, diffuse astrocytoma, anaplastic oligodendroglioma and glioblastoma.

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