Deep Learning Techniques for the Effective Prediction of Alzheimer's Disease: A Comprehensive Review

Abstract

"Alzheimer's disease" (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. "Dementia" is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person's ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. "Deep learning" (DL) is a type of "machine learning" (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered "neural network" architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field.

Keywords: Alzheimer’s disease; deep learning; health informatics.

Figure 1

Types of DL architectures.

Figure 2

The architecture of a generalized CNN.

Figure 3

MLP procedure (where Act signifies activation function, w represents weights, and Net denotes network).

Figure 4

Architecture of RBM.

Figure 5

CNN architecture.

Figure 6

RBM architecture.

Figure 7

DBN architecture.

Figure 8

AE architecture.

Figure 9

Sparse AE architecture.

Figure 10

Stacked AE architecture.