A Deep Learning Algorithm for Classifying Meningioma and Auditory Neuroma in the Cerebellopontine Angle from Magnetic Resonance Images

doi:10.11938/cjmr20192753

Abstract

Abstract: Meningioma and auditory neuroma are two types of brain tumors frequently found in the cerebellopontine angle. Misdiagnosis of the two is common due to their similarity in clinical manifestations and imaging manifestations. In this work, the deep learning algorithm was developed to classify the meningioma and auditory neuroma from the magnetic resonance images, assisting the timely and accurate diagnosis to the two brain tumors. T₁-weighted spin-echo (T₁W-SE) images were collected from 307 patients with brain tumors. Contrast limited adaptive histogram equalization (CLAHE) pre-processing was used to improve the quality of the dataset. The image features were learnt in the deep learning framework of 3-dimensional convolutional neural network (3D CNN). By optimizing the image enhancement parameters and the network structure parameters, the accuracy of the meningioma/acoustic neuroma classification model reached 0.918 0, and the area under curve (AUC) was found to be 0.913 4.

Key words: meningioma, auditory neuroma, 3-dimensional convolutional neural network (3D CNN), magnetic resonance imaging (MRI)

CLC Number:

LOU Yun-zhong, LIU Ying, JIANG Hua, ZHANG Hao-wei. A Deep Learning Algorithm for Classifying Meningioma and Auditory Neuroma in the Cerebellopontine Angle from Magnetic Resonance Images[J]. Chinese Journal of Magnetic Resonance, 2020, 37(3): 300-310.

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