Semantic context-aware attention UNET for lung cancer segmentation and classification

Lung cancer is a serious type of cancer, leading to increased mortality to death in both men and women as the symptoms are noticed only at later stages. Life span of individuals' may be extended if lung cancer is detected in its early stages. One of the imaging modalities used to diagnose lung cancer is computed tomography (CT). A nodule or mass is a small abnormal growth observed in a lung CT scan that, in most cases, may turn out to be benign. A computer-aided system is essential to help physicians in precisely diagnosing the disease. The main objective of this work is to detect and classify the nodules in the lung CT scan images as benign or malignant. A context-aware attention UNET architecture is proposed to segment the nodule from the lung CT scan image. Further, the segmented nodule is classified as benign or malignant using a Convolutional Neural Network architecture. The experiments are performed using the LUNA 16 and LIDC-IDRI lung CT scan image datasets. From the results obtained, it is observed that the context-aware attention UNET shows a noteworthy improvement in the following metrics: Dice Score, Sensitivity, Specificity, and F-Measure. A significant improvement is obtained compared to the existing systems in detecting the lesion as a benign nodule or malignant nodule. Further, an ablation study is performed to validate the significance of each component in the architecture. The experimental results have reported 98.81% and 99.15% for specificity and sensitivity, respectively, and therefore the proposed system has potential clinical value in the detection of lung cancer.