Computer-aided diagnosis: a neural-network-based approach to lung nodule detection.
In this work, we have developed a computer-aided diagnosis system, based on a two-level artificial neural network (ANN) architecture. This was trained, tested, and evaluated specifically on the problem of detecting lung cancer nodules found on digitized chest radiographs. The first ANN performs the detection of suspicious regions in a low-resolution image. The input to the second ANN are the curvature peaks computed for all pixels in each suspicious region. This comes from the fact that small tumors possess and identifiable signature in curvature-peak feature space, where curvature is the local curvature of the image data when viewed as a relief map. The output of this network is thresholded at a chosen level of significance to give a positive detection. Tests are performed using 60 radiographs taken from routine clinic with 90 real nodules and 288 simulated nodules. We employed free-response receiver operating characteristics method with the mean number of false positives (FP's) and the sensitivity as performance indexes to evaluate all the simulation results. The combination of the two networks provide results of 89%-96% sensitivity and 5-7 FP's/image, depending on the size of the nodules.
Computing Department, A Coruña University Informatics School, Campus de Elviña, Spain. manolo@dec.usc.es
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Penedo
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Carreira
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Cabello
D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D008433:Mathematics; D016571:Neural Networks, Computer; D011856:Radiographic Image Enhancement / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods / Q000706:statistics & numerical data; D012680:Sensitivity and Specificity