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• W2028532424 abstract "A computer-aided detection (CAD) system for the identification of small pulmonary nodules in low-dose and thin-slice CT scans has been developed. The automated procedure for selecting the nodule candidates is mainly based on a filter enhancing spherical-shaped objects. A neural approach based on the classification of each single voxel of a nodule candidate has been purposely developed and implemented to reduce the amount of false-positive findings per scan. The CAD system has been trained to be sensitive to small internal and sub-pleural pulmonary nodules collected in a database of low-dose and thin-slice CT scans. The system performance has been evaluated on a data set of 39 CT containing 75 internal and 27 sub-pleural nodules. The FROC curve obtained on this data set shows high values of sensitivity to lung nodules (80-85% range) at an acceptable level of false positive findings per patient (10-13 FP/scan)." @default.
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• W2028532424 date "2008-04-01" @default.
• W2028532424 modified "2023-09-23" @default.
• W2028532424 title "Lung nodule detection in low-dose and thin-slice computed tomography" @default.
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• W2028532424 doi "https://doi.org/10.1016/j.compbiomed.2008.02.001" @default.
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