# Computer-Aided Diagnosis of Alzheimer's Disease via Deep Learning Models and Radiomics Method
## 7 Conclusions  
In order to assist doctors in the accurate diagnosis of Alzheimer's disease, this paper conducted an in-depth study of multi-modal image fusion technology. First, the traditional wavelet fusion of T2-MRI and PET modal images of Alzheimer disease patients were studied, and then a wavelet fusion algorithm that is more suitable for medical image fusion was proposed. Based on the evaluation of objective evaluation parameters, we found that the improved wavelet fusion algorithm has better performance than the traditional wavelet fusion algorithm on the testing accuracy.  
This paper first improved and implemented the traditional convolution neural network and U-Net in the diagnosis of Alzheimer's disease in order to explore their diagnostic characteristics. It turned out that this modification of CNN has significantly improved the performance of CNN in processing with Alzheimer's disease medical images. Additionally, this paper first implemented a fusion image technique in Alzheimer's disease diagnosing and proved the efficiency of diagnosing using fusion images by implementing different deep learning models, including some pretrained models.  
This paper designed two image sets training with six different deep learning structures in the same training process. MRI, PET and fusion images of Alzheimer's disease were trained and tested, respectively. Results of these tests demonstrated that the accuracy of fusion image is higher than that of single mode. In a word, pixel-based multi-modal fusion images applying on convolution neural network can have an outstanding diagnostic effect on the diagnosis of Alzheimer's disease.  
Similar to the problems encountered by [PERSON] et al. [9], the author is also actively exploring solutions to the problem of incomplete data, such as using semi-supervised learning or unsupervised learning. The authors also notice that the experiment focuses on binary classification, and the task of multiple classification has not been involved, and plan to focus on it in future research.  
Conceptualization, Y.D.; methodology, Y.D. and W.B.; software, Z.T.; validation, Z.T.; formal analysis, W.B. and Z.T.; investigation, W.B.; resources, Y.D.; data curation, W.B.; writing--original draft preparation, W.B. and Z.T.; writing--review and editing, W.B.; visualization, Z.X.; supervision, W.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the Youth Program of National Natural Science Foundation of China under Grant 61902058, and in part by the Fundamental Research Funds for the Central Universities under Grant N2019002 and in part by the construction of the basic scientific research base of the Ministry of Education N2124006-3 and in part by the Youth Fund Project of National Natural Science Foundation of China, individualized evaluation method and related mechanism of left ventricular systolic function based on independent cardiac shock signal 61801104 and in part by the Research on Key Technologies of intelligent diagnosis of systemic lymphoma based on PET/CT radiomics 61872075.  
Not applicable.  
Informed Consent Statement: According to ADNI protocols, all procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the Helsinki declaration. The ADNI data collection was carried out after obtaining written informed consent from the participants. More details can be found at [[http://adni.loni.usc.edu](http://adni.loni.usc.edu)]([http://adni.loni.usc.edu](http://adni.loni.usc.edu)). Similarly, in [[https://www.ppmi-info.org](https://www.ppmi-info.org)]([https://www.ppmi-info.org](https://www.ppmi-info.org)), information about PPMI database can be found (accessed date 25 September 2019).