Acoustic neuroma classification algorithm based on mask region convolution neural network

Objective: Acoustic neuroma has similarities with other intracranial tumors in imaging manifestations and location of incidence, and misdiagnosis often occurs in clinical practice. This paper uses a mask region convolution neural network (Mask RCNN) to classify acoustic neuromas. Methods: The T1WI-SE sequence MRI images of 120 patients with acoustic neuroma in our hospital were collected. Based on preprocessing, the improved feature pyramid networks (FPN) algorithm and Mask RCNN comprehensive training were conducted, and the classification effects of different networks were compared. Results: The accuracy of the Mask RCNN classification model of ResNet101 network was 0.92, the recall rate was 0.86, the specificity was 0.89, and the mean average precision (mAP) was 0.91. Conclusion: The classification model based on Mask RCNN algorithm has a good effect on the differentiation and classification of acoustic neuroma.