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A Deep Learning Method for Brain Tumor Classification Based on Image Gradient

  • Long, Hoang (Department of Artificial Intelligence Convergence, Pukyong National University) ;
  • Lee, Suk-Hwan (Dept. of Computer Engineering, Dong-A University) ;
  • Kwon, Seong-Geun (Department of Electronics Engineering, Kyungil University) ;
  • Kwon, Ki-Ryong (Department of Artificial Intelligence Convergence, Pukyong National University)
  • Received : 2022.08.02
  • Accepted : 2022.08.25
  • Published : 2022.08.31
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Abstract

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.

Keywords

Acknowledgement

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (2020R1I1A306659411, 2020R1F1A1069124) and the Ministry of Trade, Industry and Energy for its financial support of the project titled "the establishment of advanced marine industry open laboratory and development of realistic convergence content.

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