TY - GEN
T1 - Automated localization of Epileptic Focus Using Convolutional Neural Network
AU - Feng, Cuixia
AU - Zhao, Hulin
AU - Zhang, Jun
AU - Cheng, Zhibiao
AU - Wen, Junhai
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/1/3
Y1 - 2020/1/3
N2 - Focal cortical dysplasia (FCD) is one of the most common causes of intractable epilepsy. The automatic localization of magnetic resonance (MR) images of epileptic lesions caused by FCD can be performed by using the convolutional neural network (CNN) technology in the field of artificial intelligence, which is helpful for doctors to better diagnose. This study trained a four-layer CNN, including the 8630 learning parameters and three convolutional layers followed by the pooling layer and one full connection layer, and finally, output an image as a disease probability value. The network can classify the MR images with the disease or not, and the accuracy of the final classification of the new data is 92.45%. On the basis of correct classification, the accurate rate of FCD lesions localization can reach 92.86% by using this network.
AB - Focal cortical dysplasia (FCD) is one of the most common causes of intractable epilepsy. The automatic localization of magnetic resonance (MR) images of epileptic lesions caused by FCD can be performed by using the convolutional neural network (CNN) technology in the field of artificial intelligence, which is helpful for doctors to better diagnose. This study trained a four-layer CNN, including the 8630 learning parameters and three convolutional layers followed by the pooling layer and one full connection layer, and finally, output an image as a disease probability value. The network can classify the MR images with the disease or not, and the accuracy of the final classification of the new data is 92.45%. On the basis of correct classification, the accurate rate of FCD lesions localization can reach 92.86% by using this network.
KW - Convolutional neural network
KW - Epilepsy
KW - Focal cortical dysplasia
KW - Localization
KW - MR images
UR - http://www.scopus.com/inward/record.url?scp=85083325835&partnerID=8YFLogxK
U2 - 10.1145/3378904.3378928
DO - 10.1145/3378904.3378928
M3 - Conference contribution
AN - SCOPUS:85083325835
T3 - ACM International Conference Proceeding Series
SP - 72
EP - 75
BT - BDET 2020 - 2020 2nd International Conference on Big Data Engineering and Technology
PB - Association for Computing Machinery
T2 - 2nd International Conference on Big Data Engineering and Technology, BDET 2020
Y2 - 3 January 2020 through 5 January 2020
ER -