Author/Authors :
Zhu, Hong School of Medical Information - Xuzhou Medical University - Xuzhou, China , Fang, Qianhao School of Medical Information - Xuzhou Medical University - Xuzhou, China , He, Hanzhi School of Medical Information - Xuzhou Medical University - Xuzhou, China , Hu, Junfeng School of Medical Information - Xuzhou Medical University - Xuzhou, China , Jiang, Daihong Xuzhou University of Technology - Xuzhou, China , Xu, Kai Affiliated Hospital of Xuzhou Medical University - Xuzhou, China
Abstract :
Meningioma is the second most commonly encountered tumor type in the brain. There are three grades of meningioma by the
standards of the World Health Organization. Preoperative grade prediction of meningioma is extraordinarily important for
clinical treatment planning and prognosis evaluation. In this paper, we present a new deep learning model for assisting automatic
prediction of meningioma grades to reduce the recurrence of meningioma. Our model is based on an improved LeNet-5 model of
convolutional neural network (CNN) and does not require the extraction of the diseased tissue, which can greatly enhance the
efficiency. To address the issue of insufficient and unbalanced clinical data of meningioma images, we use an oversampling
technique which allows us to considerably improve the accuracy of classification. Experiments on large clinical datasets show that
our model can achieve quite high accuracy (i.e., as high as 83.33%) for the classification of meningioma images.
Keywords :
CNN , Convolutional , Amplification , Meningioma
