卷积神经网络在MRl图像诊断中的应用

基于卷积神经网络对颅内组织器官疾病诊断提出了创新思路.选取帕金森病患者图像通过灰度映射、直方图均衡化、改进的小波去噪、图像增强等预处理，以VGG-Net网络模型为基础设计并搭建卷积神经网络，采取正则化策略避免过拟合问题，用患者MRI图像集对网络模型进行5次十折交叉验证，同时通过反卷积网络实现特征可视化，挖掘疾病潜在特征.实验结果和客观评价表明，本文搭建的网络可根据患者MRI图像实现良好的辅助诊断.

Application of Convolutional Neural Networks in Computer-Aided Diagnosis of MRI Images

Based on convolutional neural networks， this paper proposes innovative ideas for diagnosing intracranial tissue and organ diseases. The images of Parkinson’s disease patients were preprocessed by gray mapping， histogram equalization， improved wavelet denoising and image enhancement. A convolutional neural network is designed and built on the basis of VGG-Net network model. Regularization strategy is adopted to avoid over-fitting problem. Then the ten-fold cross-validation of the network model is carried out five times with MRI image set of patients. And feature visualization is performed by deconvolution network to dig out latent features of diseases. The result and objective evaluation show that the network can make a good diagnosis of MRI image for Parkinson’s disease.