2020/06/16 by Rakshith Sathish, Sathish, Rakshith, Rachana Sathish +5
Engineering · Medicine · #Advanced X-ray and CT Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Lung Cancer Diagnosis and Treatment #Machine Learning (cs.LG) #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2006.09308
openalex publication_date 2020/06/16 · openalex created_date 2022/07/19 · openalex updated_date 2026/07/28
Lung cancer is the most common form of cancer found worldwide with a high\nmortality rate. Early detection of pulmonary nodules by screening with a\nlow-dose computed tomography (CT) scan is crucial for its effective clinical\nmanagement. Nodules which are symptomatic of malignancy occupy about 0.0125 -\n0.025 % of volume in a CT scan of a patient. Manual screening of all slices is\na tedious task and presents a high risk of human errors. To tackle this problem\nwe propose a computationally efficient two stage framework. In the first stage,\na convolutional neural network (CNN) trained adversarially using Turing test\nloss segments the lung region. In the second stage, patches sampled from the\nsegmented region are then classified to detect the presence of nodules. The\nproposed method is experimentally validated on the LUNA16 challenge dataset\nwith a dice coefficient of 0.984\±0.0007 for 10-fold cross-validation.\n