Early prediction of Alzheimer's disease using convolutional neural network: a review

作者：Vijeeta Patil, Manohar Madgi, Ajmeera Kiran · 发表于：The Egyptian Journal of Neurology Psychiatry and Neurosurgery · 年份：2022 · DOI：10.1186/s41983-022-00571-w · 被引用次数：129 · 研究领域：Dementia and Cognitive Impairment Research、Artificial Intelligence in Healthcare、Brain Tumor Detection and Classification

Abstract In this paper, a comprehensive review on Alzheimer's disease (AD) is carried out, and an exploration of the two machine learning (ML) methods that help to identify the disease in its initial stages. Alzheimer's disease is a neurocognitive disorder occurring in people in their early onset. This disease causes the person to suffer from memory loss, unusual behavior, and language problems. Early detection is essential for developing more advanced treatments for AD. Machine learning (ML), a subfield of Artificial Intelligence (AI), uses various probabilistic and optimization techniques to help computers learn from huge and complicated data sets. To diagnose AD in its early stages, researchers generally use machine learning. The survey provides a broad overview of current research in this field and analyses the classification methods used by researchers working with ADNI data sets. It discusses essential research topics such as the data sets used, the evaluation measures employed, and the machine learning methods used. Our presentation suggests a model that helps better understand current work and highlights the challenges and opportunities for innovative and useful research. The study shows which machine learning method holds best for the ADNI data set. Therefore, the focus is given to two methods: the 18-layer convolutional network and the 3D convolutional network. Hence, CNNs with multi-layered fetch more accurate results as compared to 3D CNN. The work also contribute...