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.
Keywords: Neurodegenerative diseases, Alzheimer’s, Parkinson’s, MRI, Pre-processing, Deep learning, Biomarkers.
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