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http://dx.doi.org/10.9717/kmms.2022.25.8.1233

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)
Publication Information
Journal of Korea Multimedia Society / v.25, no.8, 2022 , pp. 1233-1241 More about this Journal
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
Brain Tumor Classification; Image Gradient; Deep Learning; Convolutional Neural Network;
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Times Cited By KSCI : 2  (Citation Analysis)
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