The potential advantages of using digital techniques instead of film-based radiology have been discussed very extensively for the past ten years. These advantages are found mainly in the computer management of picture archiving and communication systems (PACS). On the other hand, the computer-assisted diagnosis (CADx) could potentially enhance radiological services in the future. Lung nodule detection has been a clinically difficult subject for many years. Most of the literature has indicated that the finding rate for lung nodules (size range from 3 mm to 15 mm) is only about 65%, and 30% of the missing nodule can be found retrospectively. In the recent research, imaging processing techniques, such as thresholding and morphological analysis, have been employed to enhance the true-positive detection. However, these methods still produce many false-positive detections. We have used neural networks to distinguish true-positives from the suspected areas-of-interest which are generated from signal enhanced image. The initial results show that the trained neural networks program can increase true-positive detections and drastically reduce the number of false-positive detections. This program can perform three modes of lung nodule detection: (1) thresholding, (2) profile matching analysis, and (3) neural network. This program is fully automatic and has been implemented in a DEC 5000/200 workstation. The total processing time for all three methods is less than 35 seconds. We are planning to link this workstation to our PACS for further clinical evaluation. In this paper, we report our neural network and fast algorithms for various image processing techniques for the lung nodule detection and show the results of the initial studies.