Combined mixed Gaussian model with pattern recognition in the automatic diagnosis of Alzheimer's disease. Issue 15 (16th February 2021)

• Record Type: Journal Article
• Authors: Qian, Mingyue; Zhang, Zhaoting; Chen, Jiechun
• Is Part Of: IET image processing. Volume 14:Issue 15(2020)
• Page Start: 3698
• Page End: 3704
• Publication Date: 2021-02-16
• Subjects: diseases -- cognition -- learning (artificial intelligence) -- Gaussian processes -- pattern recognition -- patient diagnosis -- medical diagnostic computing -- statistical distributions; combined mixed Gaussian model -- automatic diagnosis -- Alzheimer's disease prevention -- classical machine learning -- pattern recognition -- patient cognition -- computer‐aided diagnosis -- diagnostic system -- probability prediction -- distribution methods; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1629
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The diagnosis and prevention of Alzheimer's disease plays an important role in improving patient cognition. It can be seen from the current situation that the diagnosis of Alzheimer's disease is still poor because it is affected by many factors. Based on this, combined with the symptoms of Alzheimer's disease, this study used computer‐aided to diagnose the symptoms of patients. First of all, this study analyses classical machine learning and chooses the appropriate model for diagnosis. Next, this study constructs a diagnostic system based on a mixed Gaussian model and uses a mixed Gaussian model to predict the probability of different distribution methods. Finally, this study designs experiments to analyse the performance of diagnostic models. Studies have shown that the mixed Gaussian model has a good effect on the automatic diagnosis of Alzheimer's disease, and can provide a theoretical reference for subsequent related research.