Lung Nodule Sizes Are Encoded When Scaling CT Image for CNN's

Abstract

Noninvasive diagnosis of lung cancer in early stages is one task where radiomics helps. Clinical practice shows that the size of a nodule has high predictive power for malignancy. In the literature, convolutional neural networks (CNNs) have become widely used in medical image analysis. We study the ability of a CNN to capture nodule size in computed tomography images after images are resized for CNN input. For our experiments, we used the National Lung Screening Trial data set. Nodules were labeled into 2 categories (small/large) based on the original size of a nodule. After all extracted patches were re-sampled into 100-by-100-pixel images, a CNN was able to successfully classify test nodules into small- and large-size groups with high accuracy. To show the generality of our discovery, we repeated size classification experiments using Common Objects in Context (COCO) data set. From the data set, we selected 3 categories of images, namely, bears, cats, and dogs. For all 3 categories a 5- × 2-fold cross-validation was performed to put them into small and large classes. The average area under receiver operating curve is 0.954, 0.952, and 0.979 for the bear, cat, and dog categories, respectively. Thus, camera image rescaling also enables a CNN to discover the size of an object. The source code for experiments with the COCO data set is publicly available in Github (https://github.com/VisionAI-USF/COCO_Size_Decoding/).