Lung nodule classification based on deep learning networks and handcraft segmentation

Abstract

This study proposes a Hybrid CAD system, where the first stage consists of the handcraft segmentation, following a CNN based on the ResNet-34 architecture. In the segmentation stage, the rib cage (thorax region) is extracted using the K-means algorithm. The extraction of the nodules is performed in two steps, those attached to the pleura are found via a hysteresis threshold on the rib cage. The circumscribed and vascular nodules are extracted using morphological operations. The resulting segmentation masks are applied to the test images, decreasing the number of false positives. Finally, the resulting image is splitted of in patches to be classified by the ResNet-34 trained from scratch. Designed CAD system has been implemented on Google Collab platform and a standalone computer with Nvidia RTX 3090. The experiments with different CAD systems were performed on SPIE and LIDC-IDRI datasets demonstrating better performance of designed technique with reduction of false-positive objects.