Neural networks for computer aided diagnosis: detection of lung nodules in chest radiograms

The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radio- grams. Our approach is based on multiscale processing and arti- ficial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an atten- tion focusing subsystem that processes whole radiographs to lo- cate possible nodular regions ensuring high sensitivity; 2) a vali- dation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image fea- tures. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private data- base with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper under- standing of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4–10, while accuracy is in the range 95.7–98.0%. When the second data set was used comparable re- sults were obtained. The observed system performances support the undertaking of system validation in clinical settings.