Abstract
Lung modules are an initial indicator as to whether or not a patient will develop lung carcinoma which has a significant mortality rate if it is not detected at an early stage. The detection of these lung nodules is a complex task and is time-consuming for the radiologist.For these reasons, CAD systems have been developed and employed for the detection of lung nodules. In this article, we present a CAD system that uses a hybrid strategy: techniques for the analysis of medical images and soft computing (fuzzy clustering, SVM and ANN) with a description of the main stages: preprocessing, identification of ROIs (region of interest), creation of VOIs (volume interests) and ROI classifications. Remarkable elements of the system are: detection automation, a new phase to reduce the ROIs false positives and a new algorithm to build the VOIs in order to improve the location and a better detection. For its development, helical CT studies proceeding from the LIDC public database have been used.The system achieves similar and even better results to other CADs for the same purpose with a sensitivity of 82% and a number of false positives of 7.3 per study.
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