Effective deep learning approach for segmentation of pulmonary cancer in thoracic CT image

Abstract

One of the most severe kinds of tumors in people is lung cancer. Identifying lung cancer and its types requires costly and time-consuming procedure research. Furthermore, lung nodules are difficult to identify because of their diversity and visual similarity to neighboring locations. Conventional machine learning methods either treat these components separately or rely on human integration, which can be time-consuming and could fail to fully capture the intricate relationships between these features. Deep learning methods' layered structures enable them to automatically incorporate many features and learn meaningful descriptions. Thus, this framework proposes an efficient deep learning technique for classifying pulmonary nodules using Computerized Tomography (CT) images. Initially, several pre-processing methods are taken into account to prepare the data. Then, T-Net based deep learning algorithm segments the lung nodule, and the CenterNet-based method extracts the texture and intensity attributes from the segmented image. Following that, the proposed NASNet-based classifier categorizes the nodules as cancerous or not, using the attributes that have been collected. Finally, the presented method will be assessed by the metrics like Dice similarity coefficient (DSC), Sensitivity, Positive predictive Value (PPV), f1-score, precision, recall, and accuracy on the LUNA-16 and Lung Image Database Consortium (LIDC-IDRI) datasets, and the outcomes are contrasted with other existing approaches.