This paper is published in Volume-5, Issue-4, 2019
Area
Information Technology
Author
Souparnika Padaki Patil
Co-authors
Dr. Anant M. Bagade
Org/Univ
SCTR'S Pune Institute of Computer Technology, Pune, Maharashtra, India
Pub. Date
12 July, 2019
Paper ID
V5I4-1174
Publisher
Keywords
Alzheimer’s Disease, Mild Cognitive Impairment, Partial Least Squares, Gaussian Mixture Model, Nonnegative Matrix Factorization, Support Vector Machine

Citationsacebook

IEEE
Souparnika Padaki Patil, Dr. Anant M. Bagade. Early prediction of Alzheimer’s Disease based on neuroimaging and deep learning: Review, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Souparnika Padaki Patil, Dr. Anant M. Bagade (2019). Early prediction of Alzheimer’s Disease based on neuroimaging and deep learning: Review. International Journal of Advance Research, Ideas and Innovations in Technology, 5(4) www.IJARIIT.com.

MLA
Souparnika Padaki Patil, Dr. Anant M. Bagade. "Early prediction of Alzheimer’s Disease based on neuroimaging and deep learning: Review." International Journal of Advance Research, Ideas and Innovations in Technology 5.4 (2019). www.IJARIIT.com.

Abstract

Alzheimer’s disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer’s disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain using resting-state functional magnetic resonance imaging (R-fMRI) data. A targeted autoencoder network is built to distinguish normal aging from mild cognitive impairment, an early stage of AD. The proposed method reveals discriminative brain network features effectively and provides a reliable classifier for AD detection. Compared to traditional classifiers based on R-fMRI time series data. The proposed work is also able to classify the different types of Alzheimer’s disease as well as a particular stage of the disease. Finally, we will compare our deep learning approach accuracy with existing systems. In this paper, we proposed a system using deep learning with brain network and clinical relevant text information to make an early diagnosis of Alzheimer’s Disease (AD). The clinical relevant text information includes age, gender and ApoE gene of the subject. The brain network is constructed by computing the functional connectivity of brain regions using resting-state functional magnetic resonance imaging (R-fMRI) data. A targeted autoencoder network is built to distinguish normal aging from mild cognitive impairment, an early stage of AD. The proposed method reveals discriminative brain network features effectively and provides a reliable classifier for AD detection.