Alzheimer’s disease is the third most expensive disease, only after cancer and cardiopathy. It is also the fourth leading cause of death in the elderly after cardiopathy, cancer, and cerebral palsy. The disease lacks specific diagnostic criteria. At present, there is still no definitive and effective means for preclinical diagnosis and treatment. It is the only disease that cannot be prevented and cured among the world’s top ten fatal diseases. It has now been proposed as a global issue. Computer-aided diagnosis of Alzheimer’s disease (AD) is mostly based on images at this stage. This project uses multi-modality imaging MRI/PET combining with clinical scales and uses deep learning-based computer-aided diagnosis to treat AD, improves the comprehensiveness and accuracy of diagnosis. The project uses Bayesian model and convolutional neural network to train experimental data. The experiment uses the improved existing network model, LeNet-5, to design and build a 10-layer convolutional neural network. The network uses a back-propagation algorithm based on a gradient descent strategy to achieve good diagnostic results. Through the calculation of sensitivity, specificity and accuracy, the test results were evaluated, good test results were obtained.
Gait pattern analysis to suggest one of factors classifying alzheimer's disease level using deep learning based on convolutional neural network
