Abstract:
Schizophrenia affects about 1% of the world population with a lifetime prevalence of 0.3-
0.66% is among the frequently occurring psychotic disorders. Schizophrenia has been
clinically identified, but there is no pathophysiology for diagnosing it. Despite the fact
that much study has been done on volumetric MRI in Schizophrenia, detecting it using
biological markers is difficult as most psychiatric diseases share the same symptoms.
The prefrontal and temporal lobes, particularly the medial and superior temporal lobes,
have been found to have decreased volume in earlier research. Predictive analytics and
clinical decision assistance both heavily rely on these findings. Analyzing enormous
medical imaging data is time consuming for experts. Additionally, drawing inferences
from these analyses may be erroneous or biased. While machine learning algorithms may
aid specialists in automated analysis to some level, they might not be able to analyze such
vast volumes of data and accurately resolve complex problems as these conventional
methods often overlook important information. We suggest employing a deep learning
approach based on the Convolutional Neural Networks (CNN) rather than the
conventional machine learning approaches. Deeper networks can learn a new, more
complex representation of the input data at each layer, allowing them to develop deep
representations. Clinicians may find it easier to distinguish schizophrenia from other
mental diseases using deep learning algorithms that have been trained on bigger data sets
with a variety of illness stages and severity.
