The detection of lung nodules located in opaque areas including the mediastinum, retrocardiac lung, and lung projected below or on the diaphragm has been very difficult, because the contrast of these nodules is usually extremely low, and sometimes radiologists may not pay attention to these locations. In this study, we have developed a new computer-aided diagnostic (CAD) scheme designed specifically for the detection of these difficult-to-detect lung nodules located in opaque areas. We used 1,000 chest images with 1,076 lung nodules, which included 73 very difficult lung nodules in these opaque areas. In this new computerized scheme, opaque areas within a chest image were segmented by use of an adaptive multi-thresholding method based on edge-gradient values, and then the gray level and contrast of the chest image were adjusted for the opaque areas. Initial candidates were identified by use of the nodule-enhanced image obtained with the average radial-gradient (ARG) filtering technique based on radial gradient values. We employed a total of 35 image features for sequential application of artificial neural networks (ANNs) in order to reduce the number of false-positive candidates. The ANNs were trained and tested by use of a k-fold cross-validation test method (k=100), in which each of 100 different combinations of training and test image data sets included 990 and 10 chest images, respectively. The overall performance determined from the results of 100 test data sets indicated that the average sensitivity in detecting lung nodules was 52.1% with 1.89 false positives per image, which was considered "acceptable", because these nodules were very subtle and difficult to detect. By combination of this advanced CAD scheme with our standard CAD scheme for lung-nodule detection, the clinical usefulness of the CAD scheme would be improved significantly.