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.
Highlights
C ANCER is one of the biggest public health problems worldwide
We proposed a Computer-Aided Detection (CADe) system based on Computed Tomography (CT) attenuation patterns for pulmonary nodule detection
We highlight that the CADe system proposed to have achieved a sensitivity of 94.90% and an average of 1.0 False Positives per scan (FP/Scan), outperforming other state-of-the-art methods
Summary
C ANCER is one of the biggest public health problems worldwide. In 2018, cancer was responsible for 9.6 million deaths globally, about one-sixth of the fatalities. Solutions to pulmonary nodule candidate detection using Deep Convolutional Neural Network (DCNN) are being proposed These solutions use learning-based features and show better results than traditional approaches [19]. This paper proposes an automated CADe system for pulmonary nodule detection in low-dose thoracic CT scan images with two major steps, NCD and FPR respectively. The rest of this paper is organized as it follows: Section II introduces the related works about automated CADe systems for pulmonary nodule candidate detection; Section III describes the proposed methodology; Section IV presents the experiments and results obtained and presents an ablation study about the classifier ensemble.
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