Article
ALZHEIMER’S DISEASE DETECTION USING DEEP LEARNING
Alzheimer's disease, a leading cause of dementia, is characterized by memory loss and neurodegenerative disorders. Early diagnosis plays a crucial role in enhancing patient care and treatment outcomes. Traditional approaches for Alzheimer's disease diagnosis have limitations in terms of efficiency and learning time. Deep learning-based approaches, particularly Convolutional Neural Networks (CNNs), have shown promise in the classification of neuroimaging data related to Alzheimer's disease. In this presentation, we explore the use of a 12-layer CNN model trained on our datasets for early detection of Alzheimer's disease. Experimental results highlight the effectiveness of our proposed approach in improving accuracy and efficiency in Alzheimer's disease detection. Our research aims to contribute to the advancement of diagnostic techniques for Alzheimer's disease through the application of deep learning algorithms. Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia in older adults. The part of brain that gets affected in this disease is hippocampus degeneration. Detection of Alzheimer’s disease at preliminary stage is very important as it can prevent serious damage to the patient’s brain. It becomes dangerous and sometimes fatal in case of people of 65 years of age or above. The main objective of this project is to use machine learning algorithms that is and feature extraction and selection to predict the Alzheimer’s disease and build a useful model. The dataset is taken in the form of images. The proposed approach detects the Alzheimer’s Disease such as moderate-demented and non demented using CNN algorithm.
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