  With recent advancements in the development of artificial intelligence
applications using theories and algorithms in machine learning, many accurate
models can be created to train and predict on given datasets. With the
realization of the importance of imaging interpretation in cancer diagnosis,
this article aims to investigate the theory behind Deep Learning and Bayesian
Network prediction models. Based on the advantages and drawbacks of each model,
different approaches will be used to construct a Bayesian Deep Learning Model,
combining the strengths while minimizing the weaknesses. Finally, the
applications and accuracy of the resulting Bayesian Deep Learning approach in
the health industry in classifying images will be analyzed.
