A Greedy Deep Learning Method for Medical Disease Analysis

This paper proposes a new deep learning method, the greedy deep weighted dictionary learning for mobile multimedia for medical diseases analysis. Based on the traditional dictionary learning methods, which neglects the relationship between the sample and the dictionary atom, we propose the weighted mechanism to connect the sample with the dictionary atom in this paper. Meanwhile, the traditional dictionary learning method is prone to cause over-fitting for patient classification of the limited training data set. Therefore, this paper adopts $\mathrm {l}_{2}$ -norm regularization constraint, which realizes the limitation of the model space, and enhances the generalization ability of the model and avoids over-fitting to some extent. Compared with the previous shallow dictionary learning, this paper proposed the greedy deep dictionary learning. We adopt the thinking of layer by layer training to increase the hidden layer, so that the local information between the layer and the layer can be trained to maintain their own characteristics, reduce the risk of over-fitting and make sure that each layer of the network is convergent, which improves the accuracy of training and learning. With the development of Internet of Things and the soundness of healthcare monitoring system, the method proposed have better reliability in the field of mobile multimedia for healthcare. The results show that the learning method has a good effect on the classification of mobile multimedia for medical diseases, and the accuracy, sensitivity, and specificity of the classification have good performance, which may provide guidance for the diagnosis of disease in wisdom medical.