Segmentation of Pulmonary Nodules Based on MRBU-Net-WD Model

It is important to diagnose lung nodules as early as possible in order to improve the cure rate of lung cancer patients. The CT technology is currently the most prevalent method of diagnosis and treatment in clinical medicine. However, the amount of data generated by CT diagnosis and treatment is increasing exponentially. In order to segment lung nodules from medical images, it is crucial to employ computer and artificial intelligence technology. The deep learning model proposed in this paper is MRBU-Net-WD model, a more effective and enhanced version of the U-Net. MRBU-Net-WD is distinguished by combining residual 3D convolution modules with multiscale densely connected modules. Moreover, In addition, in order to avoid the phenomenon of gradient disappearance when the network depth of the model is increasing. The Bi-FPN is therefore introduced to enhance the feature maps of the network at each depth, as well as to provide an effective fusion of features across depths. The weighted Dice loss function has a significant improvement for the pixel imbalance between lung nodules and background images in lung CT images. To test the proposed model, the LUNA-16 dataset has been extensively trained and evaluated. The performance of the comparison methods generally outperforms that of the original U-Net model and several other newly proposed models. © 2023,IAENG International Journal of Computer Science. All Rights Reserved.