Alzheimer’s Disease Classification On sMRI Images Using Convolutional Neural Networks And Transfer Learning Based Methods

Abstract

The most well-known cause of dementia that impairs memory is Alzheimer’s disease. Alzheimer’s patients have a neurodegenerative condition that causes loss of various brain functions. Researchers nowadays have established that a disease’s early diagnosis is the most important factor in improving patient care and treatment. Traditional methods for diagnosing Alzheimer’s disease (AD) are slow, inefficient, and require a lot of learning and training time. Recently, methods based on deep learning have been taken into consideration to classify neuroimaging information related to AD. In this research, we explore the use of transfer learning and convolutional neural networks (CNN) for AD early detection. To extract features for the classification process, we employ Alexnet that has been trained on our datasets. The success of the suggested strategy is explained by experimental research.