Improving Cancer Imaging Diagnosis with Bayesian Networks and Deep Learning: A Bayesian Deep Learning Approach
This work addresses the need for more accurate diagnostic tools in healthcare, specifically for cancer imaging, but appears incremental as it builds on existing methods without claiming major breakthroughs.
The paper tackles the problem of improving cancer imaging diagnosis by combining Bayesian networks and deep learning into a Bayesian deep learning model, aiming to enhance classification accuracy in medical images.
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.