जेरोन्टोलॉजी और जराचिकित्सा अनुसंधान

अमूर्त

Detecting Alzheimer's disease using a Hybrid Deep Learning Approach

Meraj Riga*

Alzheimer's disease primarily affects the nervous system. Neuronal atrophy, amyloid deposition, and cognitive, behavioural, and mental problems are the main hallmarks. Over the years, a variety of machine learning algorithms have been studied and used for identification, focusing on the subtle prodromal stage of mild cognitive impairment to evaluate key characteristics that distinguish the disease's early manifestation and provide guidance for early detection and treatment. Due to the difficulties in telling individuals with cognitive normalcy from from those without, early identification is still difficult. The majority of classification algorithms thus perform badly for these two categories. For the purpose of Alzheimer's disease early detection, this research suggests a hybrid Deep Learning Approach. Combining multimodal imagery with a convolutional neural network.