Lung nodule detection in chest CT images based on vision transformer network with Bayesian optimization

Abstract

PurposeLung cancer patients are usually diagnosed by the Computed tomography (CT) modality with the help of radiologists. Frequently, radiologists manually analyse CT images, which is a big task that consumes more time and results in inaccuracy. Therefore, this study proposes an automated computer-aided diagnosis (CAD) scheme based on Vision Transformer architecture with Bayesian Optimisation for lung nodule detection to assist radiologists in decision-making. MethodsThe Vision Transformer architecture relies on the shifted window-based vision transformer encoders to improve the processing and propagation efficiency of the lung nodule features. The baseline detection model is developed by integrating shifted window-based vision transformer for deep nodule feature extractions and a region proposal network to predict candidate nodules with low network complexity. The Bayesian Optimisation with the Gaussian method was used to optimise the hyperparameter values from the baseline model suitable for lung nodule detection on chest CT images. ResultsWe used 888 CT images with 10-fold cross-validation from the Lung Nodule Analysis 2016 (LUNA16) public dataset to validate the proposed network. Free-response receiver operating characteristics and competition performance metrics (CPM) was used for the performance assessment. The proposed architecture obtained the highest detection sensitivity of 98.39% and the CPM score of 0.909 with few network parameters. ConclusionThis study proposes a CAD system based on a vision transformer network determined by Bayesian Optimisation for lung nodule detection in chest CT images. The empirical results prove the proposed algorithm's effectiveness compared to the state-of-the-art methods.