Pulmonary nodule detection on chest radiographs using balanced convolutional neural network and classic candidate detection

•A combination of the classic nodule candidate detection method and the CNN method for false positive reduction is presented to achieve high performance for lung nodule detection in CXR.•The nodule enhancement ROI image, corresponding segmentation result, and original ROI image were encoded into an RGB color image instead of the duplicated original ROI image as the input of the CNN for false positive reduction.•All sub modules are designed for processing the original size of CXR to avoid missing small nodules when down sampling the CXR. Computer-aided detection (CADe) systems play a crucial role in pulmonary nodule detection via chest radiographs (CXRs). A two-stage CADe scheme usually includes nodule candidate detection and false positive reduction. A pure deep learning model, such as faster region convolutional neural network (faster R-CNN), has been successfully applied for nodule candidate detection via computed tomography (CT). The model is yet to achieve a satisfactory performance in CXR, because the size of the CXR is relatively large and the nodule in CXR has been obscured by structures such as ribs. In contrast, the CNN has proved effective for false positive reduction compared to the shallow method. In this paper, we developed a CADe scheme using the balanced CNN with classic candidate detection. First, the scheme applied a multi-segment active shape model to accurately segment pulmonary parenchyma. The grayscale morphological enhancement technique was then used to improve the conspicuity of the nodule structure. Based on the nodule enhancement image, 200 nodule candidates were selected and a region of interest (ROI) was cropped for each. Nodules in CXR exhibit a large variation in density, and rib crossing and vessel tissue usually present similar features to the nodule. Compared to the original ROI image, the nodule enhancement ROI image has potential discriminative features from false positive reduction. In this study, the nodule enhancement ROI image, corresponding segmentation result, and original ROI image were encoded into a red–green–blue (RGB) color image instead of the duplicated original ROI image as input of the CNN (GoogLeNet) for false positive reduction. With the Japanese Society of Radiological Technology database, the CADe scheme achieved high performance of the published literatures (a sensitivity of 91.4 % and 97.1 %, with 2.0 false positives per image (FPs/image) and 5.0 FPs/image, respectively) for nodule cases.