Linear Nodule Growth Pattern Prediction (LNGP) Metric for Lung Pulmonary Malignancy Classification

Despite deep learning approaches capabilities in classification problems, their performances are negatively impacted by insufficient training data, which is most often the case in medical imaging diagnosis. As a result, medical imaging diagnoses using hand-crafted feature-based approaches are still very relevant and efficient, as a stand-alone method and also as a complement to deep learning-based ones, due to generating explorable features. In this research, we devise a new metric (i.e., feature) to classify the pulmonary nodules into benign or malignant. Since benign nodules grow in a more consistent pattern than malignant ones, we hypothesize that the appearance of a benign nodule on consecutive CT image slices is highly correlated. Thus, we propose a linear-prediction-based metric, Linear Nodule Growth Pattern Prediction (LNGP), to capture and quantify any probable inter- and intra-slice correlation in expert-assisted localized nodules, toward malignancy classification. Using the LIDC-IDRI dataset, the results of malignancy classification based on the LNGP metric show AUCs of ۰.۹۹۹۸, ۰.۹۴۷۸, and ۰.۹۵۰۱ over three sub-datasets with increasing uncertainty level, respectively. Being a hand-crafted approach, LNGP-based classification requires less training data for adequate accuracy. Moreover, we show that combining the LNGP metric with conventional hand-crafted methods improves classification performance.

کلیدواژه ها:

CADx ، computed tomography ، pulmonary nodule classification ، linear prediction filter