Detection and Localization of Lung Nodules by Accelerating Multi-dimensional Images using Graph Data Science

Lung nodules are small, round, or oval-shaped growths that can develop in the lungs. They are typically detected as abnormal spots or lesions on medical imaging scans, such as chest X-rays or computed tomography (CT) scans. Malignant lung nodules have the potential to be dangerous as they can be an early sign of lung cancer, one of the greatest causes of cancer-related deaths worldwide. Detecting and localizing these nodules accurately is crucial for early prognosis and effective treatment. Timely identification of malignant nodules can significantly improve patient outcomes by enabling early intervention and potentially increasing the chances of successful treatment. A Graph Convolutional Network (GCN) model was used to create an effective lung nodule identification and localization method in order to solve this problem. The LUNA-16 dataset, which has about 888 datasets, was used to train and test the GCN model. The GCN model was trained on a subset of 90 datasets and obtained an overall accuracy of 84% by utilising deep learning and graph-based representations. When categorising benign and malignant nodules, the model displayed clear performance variations. It demonstrated its capability to successfully distinguish non-cancerous nodules from potentially hazardous ones by achieving an accuracy of 60% for benign lung nodules. The GCN model also demonstrated excellent precision in diagnosing cancerous lung nodules, with an accuracy of 94% for malignant nodules. These findings highlight the potential of the GCN model in assisting medical professionals with the detection and localization of lung nodules. The promising results obtained in this research pave the way for further advancements in lung nodule analysis, contributing to the early treatment of lung cancer. By accurately identifying and localizing lung nodules, medical practitioners can make more informed decisions regarding patient care.