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13 May 2019 Automated detection and classification for early stage lung cancer on CT images using deep learning
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Abstract
Since accurate early detection of malignant lung nodule can greatly enhance the survival of the patient, detection of early stage lung cancer with chest computed tomography (CT) scans is a major problem from the last couple of decades. Therefore, automated lung cancer detection techniques is important. However, it is a significant challenge to accurately detect lung cancer at the early stage due to substantial similarities in the structure of the benign and the malignant lung nodules. The major task is to reduce the false positive and false negative results in lung cancer detection. Recent advancements in convolutional neural network (CNN) models have improved image detection and classification for many tasks. In this study, we presented a deep learning-based framework for automated lung cancer detection. The proposed framework works in multiple stages on 3D lung CT scan images to detect and determine the malignancy of the nodules. Considering 3D nature of lung CT data and the compactness of mixed link network (MixNet), two deep 3D faster R-CNN and U-Net encoder-decoder with MixNet were designed to detect and learn the features of the lung nodule, respectively. For the classification of the nodules, the gradient boosting machine (GBM) with 3D MixNet was proposed. The proposed system was tested with manually draw radiologist contours on 1200 images obtained from LIDC-IDRI including 3250 nodules by using statistical measures. LIDC-IDRI comprises of equal number of benign and malignant lung nodules. The proposed system was evaluated on this data set in the form of sensitivity (94%), specificity (90%), area under the receiver operating curve (0.99) and obtained better results compared to the existing methods.
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Nasrullah, Jun Sang, Mohammad S. Alam, and Hong Xiang "Automated detection and classification for early stage lung cancer on CT images using deep learning", Proc. SPIE 10995, Pattern Recognition and Tracking XXX, 109950S (13 May 2019); https://doi.org/10.1117/12.2520333
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