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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.<\/jats:p>","DOI":"10.1155\/2019\/7289273","type":"journal-article","created":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T19:31:07Z","timestamp":1569958267000},"page":"1-9","source":"Crossref","is-referenced-by-count":21,"title":["Automatic Prediction of Meningioma Grade Image Based on Data Amplification and Improved Convolutional Neural Network"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1676-940X","authenticated-orcid":true,"given":"Hong","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Medical Information, Xuzhou Medical University, Xuzhou, China"},{"name":"Key Laboratory of Intelligent Industrial Control Technology of Jiangsu Province, College of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, China"}]},{"given":"Qianhao","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Medical Information, Xuzhou Medical University, Xuzhou, China"},{"name":"Key Laboratory of Intelligent Industrial Control Technology of Jiangsu Province, College of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, China"}]},{"given":"Hanzhi","family":"He","sequence":"additional","affiliation":[{"name":"School of Medical Information, Xuzhou Medical University, Xuzhou, China"},{"name":"Key Laboratory of Intelligent Industrial Control Technology of Jiangsu Province, College of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou, China"}]},{"given":"Junfeng","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Medical Information, Xuzhou Medical University, Xuzhou, China"}]},{"given":"Daihong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Industrial Control 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