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Computer-aided diagnostic (CAD) schemes often employ a filter for enhancement of lesions as a preprocessing step for improving sensitivity and specificity. The filter enhances objects similar to a model employed in the filter; e.g., a blob enhancement filter based on the Hessian matrix enhances sphere-like objects. Actual lesions, however, often differ from a simple model, e.g., a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and with inhomogeneities inside such as a spiculated one and a ground-glass opacity. Thus, conventional filters often fail to enhance actual lesions. Our purpose in this study was to develop a supervised filter for enhancement of lesions by use of a massive-training artificial neural network (MTANN) in a computer-aided diagnostic (CAD) scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to enhance actual patterns of nodules. By use of the MTANN filter, the sensitivity and specificity of our CAD scheme were improved substantially. With the database with 69 lung cancers, our CAD scheme with the MTANN filter achieve a 97% sensitivity with 6.7 false positives (FPs) per section, whereas a conventional CAD scheme with a difference-image technique achieved a 96% sensitivity with 19.3 FPs per section.