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Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss

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Document pages: 4 pages

Abstract: Lung cancer is the most common form of cancer found worldwide with a highmortality rate. Early detection of pulmonary nodules by screening with alow-dose computed tomography (CT) scan is crucial for its effective clinicalmanagement. Nodules which are symptomatic of malignancy occupy about 0.0125 -0.025 of volume in a CT scan of a patient. Manual screening of all slices isa tedious task and presents a high risk of human errors. To tackle this problemwe propose a computationally efficient two stage framework. In the first stage,a convolutional neural network (CNN) trained adversarially using Turing testloss segments the lung region. In the second stage, patches sampled from thesegmented region are then classified to detect the presence of nodules. Theproposed method is experimentally validated on the LUNA16 challenge datasetwith a dice coefficient of $0.984 pm0.0007$ for 10-fold cross-validation.

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