Use of Deep Learning Techniques in Alzheimer's Disease Diagnosis

Deep learning has demonstrated promising outcomes and efficacy when compared to classical machine learning in the domain of high-dimensional MRI brain imaging data. The integration of deep learning in the clinical domain is the subject of this article, with particular reference to Alzheimer's disease. This degenerative disorder affects long-term memory and is frequently seen in older patients who have dementia. The study thoroughly investigates several deep learning methods for diagnosing Alzheimer's. According to most research articles, convolutional Brain Networks (CNN) are the most often used and discussed method for processing brain pictures. Combining several datasets (e.g., ADNI and Desert Garden) with state-of-the-art deep learning algorithms could improve Alzheimer's prediction in its early stages. Current research provides a conclusion reached after a study and deep review of a number of sources regarding the diagnosis of Alzheimer's disease.