Alzheimer Disease Diagnosis Using Convolutional Neural Network

Abstract

Alzheimer's disease progress over several years, it slowly ruins memories and thinking ability, and eventually the capacity to carry out daily tasks, leading to full-time care. The brain shrinks and loses brain cells as a result of Alzheimer's disease. While the condition can strike at any age, most patients with Alzheimer's disease are over 65.. Alzheimer patients mostly have Symptoms like memory loss, disorientation and problems with thinking ability but vary from person to person. Although genetics, environment, and lifestyle are the most likely causes of this illness, other experts think there may be multiple causes. The term “dementia” refers to brain illnesses that impair thinking, memory, and behaviour. The most typical cause of dementia is Alzheimer's disease. Patients with Alzheimer's disease typically struggle to speak clearly, identify relatives and friends, and recognise items. They can also become angry, restless, and frustrated. As Alzheimer's disease progresses, physical issues like weakness, loss of balance, and impaired bladder and bowel control appear. In this study paper, we will introduce a convolutional neural network (CNN), which is a machine learning technique, to recognise Alzheimer's disease. On the input image, image segmentation is carried out. CNN is a group of artificial neural networks that provides a more scalable approach to image classification and identification of patterns in images. Convolution is a mathematical procedure that CNN employs in place of matrix multiplication at one layer. CNN are different from other neural networks by their exceptional performance with audio, image or speech signal inputs. CNN algorithm take an input image, assign priority to different aspects for an image to find difference between images. CNN Captures Spatial and Temporal dependencies of an image using application of relevant filters.