A novel deep learning framework for lung nodule detection in 3d CT images

Lung cancer is one of the deadliest cancers all over the world. One of the indications of lung cancers is the presence of the lung nodules which can appear individually or attached to the lung walls. The early detection of these nodules is crucial for saving the patient's lives. Machine learning and image processing techniques, generally embedded in computer-aided diagnosis (CAD) systems, might help radiologists locate and assess the risk of these nodules. Accordingly, in this paper, we present a framework for identifying pulmonary nodules in lung CT images and a convolutional neural network (CNN) approach to automatically extract the features from lung images, followed by classifying the suspicious regions as either nodule or non-nodule objects. The proposed model is based on Le-Net architectural stylization and the light model is obtained after going through the innovative steps. A subset of LIDC public dataset including N = 7072 CT slices of varying nodule sizes (1 mm to 5 mm) is used to train and validate this approach. The proposed framework carries out all stages of lung segmentation as well as diagnosis and categorization of the existing nodules automatically. Training and validation steps of this network with configurations 2.4GHz Core i5 processor, 8GB memory, and Intel Graphics 520 are performed approximately in six hours and this system yields the performance with accuracy = 90.1%, sensitivity = 84.1%, specificity =91.7%, for identifying the nodules. Compared to other famous CNN architectures, the proposed model is agile (light and fast) and has appropriate performance, thereby is suitable for real-time medical image analysis.

Automated summary

The paper presents a framework for identifying pulmonary nodules in lung CT images and uses a convolutional neural network (CNN) approach to automatically extract features and classify suspicious regions. The proposed system achieves high accuracy and sensitivity, making it suitable for real-time medical image analysis after training and validation.