Abstract

In this article, we discuss operational aspects of deep learning solutions for Alzheimer’s disease. First, we introduce clinical and neural aspects of Alzheimer’s disease. After that, we discuss traditional computer-aided diagnosis methods, such as support vector machines, random forests, and logistic regressions, which use statistical and machine learning techniques to identify and predict Alzheimer’s disease. We then describe basic operational aspects of the use of deep learning, and how they provide some benefits over traditional computer-aided diagnosis methods. Finally, we describe the advantages and limitations of using deep learning, and future directions on the applications of deep learning to Alzheimer’s disease.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.