Lung cancer is the leading cause of cancer deaths worldwide. Early diagnosis is critical in increasing the 5-year survival rate of lung cancer, so the efficient and accurate detection of lung nodules, potential precursors to lung cancer, is evermore important. In this paper, a computer-aided lung nodule detection system using convolution neural networks (CNN) and handcrafted features for false positive reduction is developed. The CNNs were trained with three types of images: lung CT images, their nodule-enhanced images, and their blood vessel-enhanced images. For each nodule candidate, nine 2D patches from differently oriented planes were extracted from each type of images. Patches of the same orientation from the same type of image across different candidates were used to train the CNNs independently, which were used to extract 864 features. 88 handcrafted features including intensity, shape, and texture features were also obtained from the lung CT images. The CNN features and handcrafted features were then combined to train a classifier, and a support vector machine was adopted to achieve the final classification results. The proposed method was evaluated on 1004 CT scans from the LIDC-IDRI database using 10-fold cross-validation. Compared with the traditional CNN method using only lung CT images, the proposed method boosted the sensitivity of nodule detection from 89.0% to 90.9% at 4 FPs/scan and from 71.6% to 78.2% at 1 FP/scan. This indicates that a combination of handcrafted features and CNN features from both lung CT images and enhanced images is a promising method for lung nodule detection.
Lung cancer is the first killer among the cancer deaths. Malignant lung nodules have extremely high mortality while
some of the benign nodules don't need any treatment .Thus, the accuracy of diagnosis between benign or malignant
nodules diagnosis is necessary. Notably, although currently additional invasive biopsy or second CT scan in 3 months
later may help radiologists to make judgments, easier diagnosis approaches are imminently needed. In this paper, we
propose a novel CAD method to distinguish the benign and malignant lung cancer from CT images directly, which can
not only improve the efficiency of rumor diagnosis but also greatly decrease the pain and risk of patients in biopsy
collecting process. Briefly, according to the state-of-the-art radiomics approach, 583 features were used at the first step
for measurement of nodules' intensity, shape, heterogeneity and information in multi-frequencies. Further, with Random
Forest method, we distinguish the benign nodules from malignant nodules by analyzing all these features. Notably, our
proposed scheme was tested on all 79 CT scans with diagnosis data available in The Cancer Imaging Archive (TCIA)
which contain 127 nodules and each nodule is annotated by at least one of four radiologists participating in the project.
Satisfactorily, this method achieved 82.7% accuracy in classification of malignant primary lung nodules and benign
nodules. We believe it would bring much value for routine lung cancer diagnosis in CT imaging and provide
improvement in decision-support with much lower cost.