Optimized feature selection-based clustering approach for computer-aided detection of lung nodules in different modalities

Early detection of pulmonary lung nodules plays a significant role in the diagnosis of lung cancer. Computed tomography (CT) and chest radiographs (CRs) are currently being used by radiologists to detect such nodules. In this paper, we present a novel cluster-based classifier architecture for lung nodule computer-aided detection systems in both modalities. We propose a novel optimized method of feature selection for both cluster and classifier components. For CRs, we make use of an independent database comprising of 160 cases with a total of 173 nodules for training purposes. Testing is implemented on a publicly available database created by the Standard Digital Image Database Project Team of the Scientific Committee of the Japanese Society of Radiological Technology (JRST). The JRST database comprises 154 CRs containing one radiologist-confirmed nodule in each. In this research, we exclude 14 cases from the JRST database that contain lung nodules in the retrocardiac and subdiaphragmatic regions of the lung. For CT scans, the analysis is based on threefold cross-validation performance on 107 cases from publicly available dataset created by Lung Image Database Consortium comprised of 280 nodules. Overall, with a specificity of 3 false positives per case/patient on average, we show a classifier performance boost of 7.7% for CRs and 5.0% for CT scans when compared to a single aggregate classifier architecture.