Early Detection of Neurodegenerative Diseases using Deep Learning Techniques: Issues & Challenges.

Our body’s control center is the brain. Newer and newer brain disorders are being identified as time goes on. The cumulative loss of immunological and neuronal function is what leads to neurodegenerative diseases including Alzheimer’s, Parkinson’s, Hunting ton’s, and many others. According to recent trends, there will be 152 million peoples with various neurodegenerative diseases in 2050. In recent years, the use of artificial intelligence (AI), specifically deep learning (DL) has transformed the area of neurology. This paper introduces and discusses various deep learning algorithms, including Deep belief networks (DBN), Convolutional neural networks (CNN), Recurrent neural networks (RNN) and Autoencoders along with their theory and applications in neurological diseases prediction. This paper summarizes and highlights the most promising deep learning models for Alzheimer’s disease (AD) diagnosis and risk factors by training on neuroimaging datasets combining PET and MRI images gathered using diverse modalities. This study also reviews the entire MRI processing chain—from acquisition to image retrieval, from segmentation to disease prediction which has been optimized for deep learning. Deep learning models are not meant to replace healthcare professionals, but rather to aid in recognizing illnesses and better decision-making. The ultimate goal is to help healthcare providers make more accurate diagnoses and intervene more quickly. [ABSTRACT FROM AUTHOR]