Title:Application of Deep Learning in the Diagnosis of Alzheimer’s and Parkinson’s
Disease: A Review
Volume: 20
Author(s): Asokan Suganya and Seshadri Lakshminarayanan Aarthy*
Affiliation:
- School of Computer Science and Engineering, VIT University, Vellore, Tamil Nadu, India
Keywords:
Neurodegenerative diseases, Alzheimer’s, Parkinson’s, MRI, Pre-processing, Deep learning, Biomarkers.
Abstract: Most neurodegenerative diseases such as Alzheimer's and Parkinson's are life-threatening, critical, and incurable affecting mainly the elderly
population. Early diagnosis is challenging as disease phenotype is very crucial for predicting, preventing the progression, and effective drug
discovery. In the last few years, Deep learning (DL) based neural networks are the state-of-the-art models deployed in industries and academics
across different areas like natural language processing, image analysis, speech recognition, audio classification, and many more It has been slowly
realized that they have a high potential in medical image analysis and diagnostics and medical management in general. As this field is vast and
expanding rapidly, we have put focused on existing DL-based models to detect Alzheimer’s and Parkinson's in particular. This study gives a
summary of related medical examinations for these diseases. Frameworks and applications of many deep learning models have been discussed. We
have given precise notes on pre-processing techniques used by various studies for MRI image analysis. An overview of the application of DLbased
models in different stages of medical image analysis has been conferred. It has been realized from the review that more studies are focused
on Alzheimer's compared to Parkinson's disease Additionally, we have tabulated the various public datasets available for these diseases. We have
highlighted the potential use of a novel biomarker for the early diagnosis of these disorders. Also, some challenges and issues in implementing
deep learning techniques for the detection of these diseases have been addressed. Finally, We concluded with some future research directions
regarding deep learning techniques for diagnosis of the above diseases.
