Brain tumor screening using adaptive gamma correction and deep learning
Huang Z(黄钲)1,2,3; Song GL(宋国立)1,2; Zhao YW(赵忆文)1,2
2019
会议日期October 23-25, 2019
会议地点Beijing, China
关键词CNN Adaptive Gamma Correction
页码47-53
英文摘要Generally, the brain tumor is regarded as one of the most dangerous diseases. It is always too late to detect the brain tumors, as the tumors at the early stage are always ignored. In fact, the traditional manual diagnosis process is inefficient. The radiologists have to accomplish a great amount of reading work per day, which can result in weariness and thus lead to misdiagnosis. To liberate radiologists from endless work, a brain tumor screening system based on adaptive gamma correction and deep learning is proposed. The brain images are labeled with "non-Tumor" and "tumors", and the radiologists just needs to deal with the brain images labeled with "tumors", which can significantly reduce the workload of the radiologists. Firstly, sufficient contrast enhanced T1-weighted brain images are collected. Further, background removal based on iterative threshold and a novel adaptive gamma correction (NAGC) are implemented to generate brain images with similar overall intensity. Finally, data augmentation technologies are applied to enlarge the training set, and convolutional neural network (CNN) is adopted to train the classifier. The results indicate that the accuracy of the proposed system can reach 95.13%.
源文献作者Beijing University of Technology
产权排序1
会议录ICBBS 2019 - Proceedings of 2019 8th International Conference on Bioinformatics and Biomedical Science
会议录出版者ACM
会议录出版地New York
语种英语
ISBN号978-1-4503-7251-0
内容类型会议论文
源URL[http://ir.sia.cn/handle/173321/26194]  
专题沈阳自动化研究所_机器人学研究室
通讯作者Huang Z(黄钲)
作者单位1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China
2.Shenyang Institute of Automation, Chinese Academy of Sciences, China
3.University of Chinese Academy of Sciences, NO. 114, Nanta Street, Shenyang, Liaoning, China
推荐引用方式
GB/T 7714
Huang Z,Song GL,Zhao YW. Brain tumor screening using adaptive gamma correction and deep learning[C]. 见:. Beijing, China. October 23-25, 2019.
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