Detection of lung nodules using support Vector Machine
Abstract
Lung cancer is a disease of high mortality worldwide. Therefore, early diagnosis and treatment can save lives. Lung cancer appears as a solitary nodule on chest x-ray, which is sometimes very difficult to detect for the human eye. Therefore, developing a computer-aided diagnosis (CAD) system for the detection of lung nodules, using machine learning (ML) could have a significant impact on patient prognosis. The proposed algorithm begins by pre-processing the images to improve their quality. The lung area is then segmented by thresholding. In the next step, nodule candidates are determined using a sliding band filter and segmented by applying a threshold algorithm, based on adaptive distance (ADT). Next, the suspicious areas are processed by a support vector machine (SVM), based on 15 shape and texture characteristics. Three SVM models were trained and validated with images from a public JSRT database. The best result was obtained with the radial base model (87 % sensitivity). This performance is valued as favorable with respect to human performance.
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References
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