Lung nodule classification based on deep learning networks and handcraft segmentation

Luis G. Salvador-Torres; Jose A. Almaraz-Damian; Volodymyr I. Ponomaryov; Rogelio Reyes-Reyes; Clara Cruz-Ramos

doi:10.1117/12.2618176

27 May 2022 Lung nodule classification based on deep learning networks and handcraft segmentation

Luis G. Salvador-Torres, Jose A. Almaraz-Damian, Volodymyr I. Ponomaryov, Rogelio Reyes-Reyes, Clara Cruz-Ramos

Proceedings Volume 12102, Real-Time Image Processing and Deep Learning 2022; 121020G (2022) https://doi.org/10.1117/12.2618176
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Luis G. Salvador-Torres, Jose A. Almaraz-Damian, Volodymyr I. Ponomaryov, Rogelio Reyes-Reyes, and Clara Cruz-Ramos "Lung nodule classification based on deep learning networks and handcraft segmentation", Proc. SPIE 12102, Real-Time Image Processing and Deep Learning 2022, 121020G (27 May 2022); https://doi.org/10.1117/12.2618176

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