Lung Nodule Classification Using Combined Deep and Spectral 3D Shape Features

Abstract

Accurate diagnosis of lung nodules is essential for detection and assessment of lung cancer. The present contribution proposes a descriptive model for diagnostic classification of lung nodules by jointly using deep and spectral features from the 3D surface structure of nodules. To the best of our knowledge, this is the first work that utilizes a point cloud (PC)-based deep network for extracting nodule shape features. The PC-based deep network takes into account the 3D context of a nodule; meanwhile, it is extensively less computationally intensive. The spectral features prevent over-fitting, a common problem of deep networks trained by relatively small dataset in the medical imaging domain, and compensates for missing information of mesh connections. Experimental results reveal that our descriptive model demonstrates high sensitivity (87.23%) as well as high specificity (89.80%) with a total accuracy of 88.54% for reliable and accurate prediction of lung nodule malignancy.