Improving Cancer Imaging Diagnosis with Bayesian Networks and Deep Learning: A Bayesian Deep Learning Approach

TL;DR

This paper proposes a Bayesian Deep Learning model that combines Bayesian Networks and Deep Learning to improve the accuracy of cancer image diagnosis, leveraging the strengths of both methods.

Contribution

It introduces a novel Bayesian Deep Learning approach that integrates Bayesian Networks with Deep Learning for enhanced cancer imaging diagnosis.

Findings

Improved classification accuracy in cancer imaging

Effective combination of Bayesian Networks and Deep Learning

Potential for better diagnostic support in healthcare

Abstract

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.