International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Transfer Learning Using Convolutional Neural Network Architectures for Glioma Classification from MRI Images

  • Kulkarni, Sunita M. (Department of ECE, Sathyabama Institute of Science and Technology) ;
  • Sundari, G. (Department of ECE, Sathyabama Institute of Science and Technology)
  • Received : 2021.02.05
  • Published : 2021.02.28
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Abstract

Glioma is one of the common types of brain tumors starting in the brain's glial cell. These tumors are classified into low-grade or high-grade tumors. Physicians analyze the stages of brain tumors and suggest treatment to the patient. The status of the tumor has an importance in the treatment. Nowadays, computerized systems are used to analyze and classify brain tumors. The accurate grading of the tumor makes sense in the treatment of brain tumors. This paper aims to develop a classification of low-grade glioma and high-grade glioma using a deep learning algorithm. This system utilizes four transfer learning algorithms, i.e., AlexNet, GoogLeNet, ResNet18, and ResNet50, for classification purposes. Among these algorithms, ResNet18 shows the highest classification accuracy of 97.19%.

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References

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