Hybrid Lung Nodules Detection Network (HLNDNet): A Deep Learning Approach

In this work, an efficient Hybrid Lung Nodules Detection Network (HLNDNet) is developed for early lung cancer diagnosis using the Hybrid Deep Leaning Approach (HDLA). The effect of hybridizing deep learning architectures, such as Visual Geometric Group (VGG)-16, AlexNet, and GoogleNet, has not been thoroughly examined, even though these designs have been extensively researched for various image processing applications. Hence, this work designs an HDLA to detect and classify lung nodules using Computed Tomography (CT) images. The important modules include preprocessing, lung region segmentation, and lung nodule detection by a hybrid approach. Wiener filter and morphological operations are employed in preprocessing modules to filter noise and enhance lung structures. In the second module, the lungs are segmented, and the lung’s external components are removed using the blob detection technique. The proposed semantic image segmentation is employed independently using VGG-16, AlexNet, and GoogleNet in the last module. It labels each pixel in the lung region as either normal or abnormal (lung nodule). In the last module, the outputs of the architectures are hybridized using a Weighted Voting Approach (WVA) to reduce the false positives. The HLNDNet’s performance is evaluated using CT images of 130 patients from the database of Lung Image Database Consortium (LIDC) with three performance metrics: Intersection over Union (IoU), Class Accuracy (CA), and Pixel Accuracy (PA). Results show that the proposed HLNDNet attains an impressive PA of 98.39%, mean CA of 90.42%, and mean IoU of 78.68% for detecting lung nodules, as the hybrid approach helps improve the performance metrics over their counterparts.