Defeating Alzheimer's: AI Perspective from Diagnostics to Prognostics

Abstract

This chapter examines the various applications of deep learning algorithms in the diagnosis of Alzheimer's disease, including convolutional neural networks, automatic image segmentation, autoencoders, ensemble learning, and transfer learning. The review also discusses the benefits and drawbacks of these approaches, as well as their performance in various studies. The conclusion summarizes the key contributions of the reviewed studies and discusses the challenges and opportunities for future research in this area. Some of the key challenges and opportunities include the need for larger and more diverse datasets, the interpretability of the results, and the ethical implications of using artificial intelligence (AI) in healthcare. Future research will need to address these challenges and explore new approaches to improve the performance and reliability of deep learning algorithms in the diagnosis of Alzheimer's disease.

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