  Alzheimers Disease AD is an acute neuro disease that degenerates the brain
cells and thus leads to memory loss progressively. It is a fatal brain disease
that mostly affects the elderly. It steers the decline of cognitive and
biological functions of the brain and shrinks the brain successively, which in
turn is known as Atrophy. For an accurate diagnosis of Alzheimers disease,
cutting edge methods like machine learning are essential. Recently, machine
learning has gained a lot of attention and popularity in the medical industry.
As the illness progresses, those with Alzheimers have a far more difficult time
doing even the most basic tasks, and in the worst case, their brain completely
stops functioning. A persons likelihood of having early-stage Alzheimers
disease may be determined using the ML method. In this analysis, papers on
Alzheimers disease diagnosis based on deep learning techniques and
reinforcement learning between 2008 and 2023 found in google scholar were
studied. Sixty relevant papers obtained after the search was considered for
this study. These papers were analysed based on the biomarkers of AD and the
machine-learning techniques used. The analysis shows that deep learning methods
have an immense ability to extract features and classify AD with good accuracy.
The DRL methods have not been used much in the field of image processing. The
comparison results of deep learning and reinforcement learning illustrate that
the scope of Deep Reinforcement Learning DRL in dementia detection needs to be
explored.
