A Comprehensive Overview of the Novel Approach to Detect Alzheimer’s Disease using Deep Learning and Convolutional Neural Network

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss. Early and accurate diagnosis of AD is crucial for effective treatment and intervention. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have emerged as powerful tools for automated disease diagnosis from medical imaging data. This abstract presents a promising approach utilizing deep learning and CNNs for Alzheimer’s detection. The proposed method involves leveraging neuroimaging data, such as magnetic resonance imaging (MRI) scans, to develop a reliable and automated AD diagnostic system. Initially, the MRI scans are pre-processed to enhance image quality and remove noise. Subsequently, a CNN architecture is designed and trained using a large dataset of both AD and non-AD images. The CNN learns intricate features and patterns directly from the data, enabling it to distinguish AD- related structural abnormalities and identify potential biomarkers. The trained CNN model is then employed for AD diagnosis by inputting new, unseen MRI scans. The model applies its learned knowledge to extract relevant features from the input images and generates a diagnostic output indicating the likelihood of AD presence. The diagnostic output can be further validated and interpreted by medical professionals, assisting them in making informed decisions regarding patient care and treatment strategies.