Multi-class brain tumor classification using residual network and global average pooling

• Published in: Multimedia tools and applications Vol. 80; no. 9; pp. 13429 - 13438
• Main Authors: Kumar, R Lokesh, Kakarla, Jagadeesh, Isunuri, B Venkateswarlu, Singh, Munesh
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2021; Springer Nature B.V
• Subjects: Artificial neural networks; Brain; Brain cancer; Classification; Computer Communication Networks; Computer Science; Data augmentation; Data Structures and Information Theory; Image classification; Magnetic resonance imaging; Medical imaging; Model accuracy; Multimedia Information Systems; Performance measurement; Special Purpose and Application-Based Systems; Tumors; Deep learning; Multi-class classification; Global average pooling; Brain tumor classification; Residual network
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-10335-4

A rapid increase in brain tumor cases mandates researchers for the automation of brain tumor detection and diagnosis. Multi-tumor brain image classification became a contemporary research task due to the diverse characteristics of tumors. Recently, deep neural networks are commonly used for medical image classification to assist neurologists. Vanishing gradient problem and overfitting are the demerits of the deep networks. In this paper, we have proposed a deep network model that uses ResNet-50 and global average pooling to resolve the vanishing gradient and overfitting problems. To evaluate the efficiency of the proposed model simulation has been carried out using a three-tumor brain magnetic resonance image dataset consisting of 3064 images. Key performance metrics have used to analyze the performance of the proposed model and its competitive models. We have achieved a mean accuracy of 97.08% and 97.48% with data augmentation and without data augmentation, respectively. Our proposed model outperforms existing models in classification accuracy.