@Article{JICS-15-016, author = {Ziyue Wang}, title = {Robust nonlinear multimodal classification of Alzheimer's disease based on GMM}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {15}, number = {1}, pages = {016--021}, abstract = { Accurate diagnosis of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI) is very important for patients and clinicians. There are many useful medical data have been discovered to be remarkable for diagnosis i.e., structural MR imaging (MRI), functional imaging (e.g., FDG-PET and FIB-PET). Multimodal classification model is needed to combine these biomarkers to improve the diagnose performance. Some methods have been proposed such as linear mixed kernel, combined embedding and nonlinear graph fusion. These methods have efficiently employed the multimodal data, but they ignore the influence of noise and outliers. Noise is easily generated in image analysis and measurement. To enhance robustness, mixture distributions were applied in nonlinear regression models. Gaussian mixture model is successfully applied in many domains. In this paper, we generalize nonlinear multimodal classification model based on GMM. The performance on real dataset: 22 AD, 23 MCI and 25 NC (health) is comparable to other methods. }, issn = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22393.html} }