基于改进的L-BFGS稀疏降噪自编码网络的MRI脑图像识别方法

随着人类科技的飞速发展以及医学影像设备的不断更新，医学影像技术在脑部病 变的辅助诊断中起到了越来越重要的作用，为此，提出一种基于改进的 L-BFGS 稀疏降噪自编 码网络模型(ILSDAE)，并将其应用于 MRI 脑图像的阿尔茨海默病的识别与脑部疾病的辅助诊 断。实验数据源取自 ADNI 数据集，经过校正、配准、分割、平滑等操作，获得脑部灰质图像， 随后将改进的无监督贪婪预训练方法和 L-BFGS 算法相结合，对深度自编码网络进行训练并通 过 Softmax 回归训练学习特征，从而实现对病症患者脑部图像的识别。ILSDAE 网络模型具有 很好的鲁棒性，与堆栈式自编码和自学习方法相比，实验结果证明了所提方法的有效性。

MRI Brain Image Recognition Method Based on Improved L-BFGS Sparse Denoising Autoencoder

With the rapid development of human science and technology and the continuous updating of medical imaging equipment, medical imaging technology plays an increasingly important role in the auxiliary diagnosis of brain lesions. An improved L-BFGS (limited memory Broyden-Fletcher- Goldfarb-Shanno) sparse denoising self-coding network model is proposed and applied to the recognition of Alzheimer’s disease (AD) in MRI brain images. The experimental data source is taken from the ADNI data set, and the original data is corrected, registered, segmented, smoothed, etc. to obtain gray matter images of the brain. Then, the improved unsupervised greedy pre-training method and L-BFGS algorithm are combined to train the deep self-coding network as well as the learning features through Softmax regression training, so as to realize the brain mapping of patients with symptoms image recognition. ILSDAE network model is of good robustness, and compared with the stack self-coding and self-learning methods, the experimental results show the effectiveness of the proposed method.