Classifier Ensemble Based on Computed Tomography Attenuation Patterns for Computer-Aided Detection System

Abstract

Cancer is one of the leading causes of mortality worldwide, specifically lung cancer. Computer-Aided Detection (CADe) systems are being proposed to assist radiologists in the task of pulmonary nodule detection. In this paper, we propose a CADe system that uses Deep Convolutional Neural Network (DCNN). In the Nodule Candidate Detection (NCD) step, we used Mask Region-Convolutional Neural Network (Mask R-CNN) to detect bounding boxes in 2D slices of low-dose Computed Tomography (CT) scans. In the False Positive Reduction (FPR) step, we used a classifier ensemble based on CT attenuation patterns to boost 3D pulmonary nodule classification performance. The final confidence index generated by the CADe system to the pulmonary nodule candidates is the average of the prediction obtained with the NCD and FPR steps. The CADe system was validated on the publicly available LUng Nodule Analysis 2016 (LUNA16) challenge and obtained a sensitivity of 94.90% and an average of 1.0 False Positives per scan (FP/Scan), against 96.90% of the proposal that combines different existing CADe systems. To the best of our knowledge, our proposal has one of the best results of CADe systems, outperforming other state-of-the-art individual methods.