Multiscale 3D TransUNet-aided Tumor Segmentation and Multi-Cascaded Model for Lung Cancer Diagnosis System from 3D CT Images with Fused Feature Pool Formation

A deadly disease that affects people in various countries in the world is lung cancer (LC). The rate at which people die due to LC is high because it cannot be detected easily at its initial stage of tumor development. The lives of many people who are affected by LC are assured if it is detected in the initial stage. The diagnosis of LC is possible with conventional computer-aided diagnosis (CAD). The process of diagnosis can be improved by providing the associated evaluation outcomes to the radiologists. Since the results from the process of extraction of features and segmentation of lung nodule are crucial in determining the operation of the traditional CAD system, the results from the CAD system highly depend on these processes. The LC classification from computed tomography (CT) images of three dimensions (3D) using a CAD system is the key aspect of this paper. The collection of the 3D-CT images from the standard data source takes place in the first stage. The obtained images are provided as input for the segmentation stage, in which a multi-scale 3D TransUNet (M-3D-TUNet) is adopted to get the precise segmentation of the LC images. A multi-cascaded model that incorporates residual network (ResNet), visual geometry group (VGG)-19, and DenseNet models is utilized to obtain the deep features from the segmented images. The segmented image from the M-3D-TUNet model is given as input to this multi-cascaded network. The features are obtained and fused to form the feature pool. The feature-pool features are provided to the enhanced long short-term memory with attention mechanism (ELSTM-AM) for classification of the LC. The ELSTM-AM classifies the images as normal or healthy segments. The classifier's parameters are optimized with the help of the modified fluctuation-based queuing search algorithm (MF-QSA). The output from implementing the suggested model on 3D-CT images from Lung Nodule Analysis of 2016, with a sample of 888 CT scans with 1186 nodules dataset, achieved; Accuracy 90.9%, Precision 91.1%, Sensitivity 91%, Specificity 90.8%, and F-Score 91%, which shows that the generated framework for LC detection is better than existing models for LC classification.