Modern Survey on Alzheimer's Disease Detection and Classification based on Deep Learning Techniques

• Published in: 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) pp. 0246 - 0254
• Main Authors: Pallikonda, Anil Kumar, Varma, P Suresh
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12.10.2022
• Subjects: Alzheimer's disease; Deep learning; External stimuli; Measurement; Medical services; Mild Cognitive Impairment; Mobile communication; Support vector machines; Training
• ISSN: 2644-3163
• DOI: 10.1109/IEMCON56893.2022.9946539

Alzheimer's disease (AD) is a neurodegenerative disease that primarily affects and eventually leads to the death. Mild Cognitive Impairment (MCI) is associated with an increased risk of Alzheimer's disease. In addition to memory, it influences human behaviour, movement, and responses to external stimuli. In addition, Alzheimer's disease disrupts neuronal connections and degrades brain cells. Fewer training cases and a large number of possible functional representations are key challenges in properly diagnosing AD using existing classification schemes. Normal cognitive function (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) are the three types of Alzheimer's disease (AD). This paper describes how to use deep learning to diagnose and classify Alzheimer's disease (AD). Deep learning method is one type of machine learning technique. The performance of individual methods is assessed using various metrics. This survey is effective in determining the best classification technique for Alzheimer's disease prediction (AD). In this survey parameters like accuracy, precision, F1 score and recall are determined by the various researchers.