Use of a combined artificial intelligent system for lung cancer stage prediction and classification.

Abstract

e20037 Background: To date, the investigative set for lung cancer primarily comprises of radiological imaging modalities such as chest radiographs and computed tomography images. An increasing number of evidence underscores the pivotal role of screening programs, particularly among high-risk populations, in the early detection of lung cancer. Nevertheless, there has not been a development of a robust, structured screening protocol. In this arena, artificial intelligence (AI) emerges as a promising and potent adjunctive tool. AI systems have shown the capacity to augment the precision of early detection of lung malignancies. Methods: Bayesian Inference in Deep Learning .To model the uncertainty associated with our neural network predictions, we use the predictive distribution: P(Y∗|X∗, D) = P(Y∗|X∗, W)P(W|D)dW. Results: Posterior Distribution of Weights, in the context of Bayesian deep learning, the primary goal was to capture the distribution over the weights, of the neural network given observed. The trace-plots, which offer a visual representation of the Markov chain’s convergence, exhibit excellent mixing and stability, further solidifying the robustness of our Bayesian approach. In this context, the results clearly indicate the superiority of the BNN, with a extremely high accuracy of 99%. Moreover, class 1 (medium) in the BNN achieved a flawless score across all metrics, highlighting its capability to achieve nuanced classifications with a high degree of confidence. Conclusions: In the current rapidly evolving landscape of lung cancer diagnostics, the need for precision, interpretability and reliable quantification of uncertainty is of paramount importance. With the alarming increase in the prevalence of lung cancer worldwide, the diagnostic modalities that could enhance early detection and accurate staging become vital. The results of our study underpin the immense potential of incorporating Bayesian principles into deep neural networks, by effectively merging the robustness of traditional machine learning with the probabilistic rigor of Bayesian inference. Our work distinctly demonstrates the Bayesian Neural Networks (BNNs), particularly when paired with efficient Markov Chain Monte Carlo techniques like the Hamiltonian Monte Carlo (HMC) can offer a compelling advantage to medical aspects. By providing not only precise predictions but also a clear measure of the associated uncertainty, the BNNs can play a transformative role in lung cancer diagnosis. The superior performance of the BNN model in our experiments, particularly its 99% accuracy accentuates its potential as a diagnostic tool. Furthermore, the proficient application of the HMC technique in our Bayesian framework, as evidenced by the excellent trace-plots assures the robustness of the method.